A path planning and three-dimensional packing integrated optimization system

Abstract

The application discloses a path planning and three-dimensional packing integrated optimization system, aiming at solving the problem of resource waste and low efficiency caused by the separation of traditional path planning and three-dimensional packing optimization in the logistics industry. The system realizes the collaborative optimization of path planning and three-dimensional packing by integrating genetic algorithm and local search intelligent algorithm, and introduces time window constraints to meet the timeliness requirements of orders. The system contains five modules: data management, optimization algorithm, decision-making process, result output and supply and demand auction. The data management module is responsible for information input, verification and storage; the optimization algorithm module integrates a multi-stage decision algorithm model, which can efficiently plan paths and pack three-dimensionally; the decision-making process module assists dispatchers in making key decisions; the result output module provides visual display and comprehensive reports; the supply and demand auction module realizes efficient transaction matching between supply and demand based on the improved GS matching algorithm and multi-round auction mechanism. The application improves the loading rate and reduces operating costs while ensuring the timeliness of distribution, and is suitable for e-commerce logistics scenarios, especially in handling large-scale orders, which can significantly improve logistics efficiency and support fine management of costs and carbon footprint.

Description

Technical Field

[0001] This invention relates to intelligent scheduling technology in the logistics field, specifically a path planning and three-dimensional packing collaborative optimization system based on multi-objective optimization, which improves logistics efficiency through an intelligent algorithm that combines genetic algorithm and local search. Background Technology

[0002] In the logistics industry, route planning and 3D packing are key aspects of warehouse management. Traditional methods struggle to achieve optimal results for both route planning and 3D packing, leading to conflicts between time-sensitive orders and full-load optimization, as well as issues such as high-density loading impacting delivery time. Furthermore, the separation of route planning and packing optimization in traditional methods results in resource waste.

[0003] Currently, patents integrating route optimization and 3D packing, such as CN202210592147.9 and CN201710453009.1, do not consider the time window requirements of orders in their route planning algorithms. Patent CN202310347524.7 uses step-by-step optimization, resulting in low efficiency. Furthermore, these are all methodological patents and do not form a system with additional interactive decision-making processes and visualization modules. To improve logistics service levels and reduce transportation costs, a collaborative optimization technology incorporating time window constraints is essential, and an integrated system can significantly improve ease of use. Summary of the Invention

[0004] This invention provides an integrated system for route planning and 3D packing, which integrates route planning and 3D packing technologies to achieve sufficient cargo loading and ensure rapid order response. The system includes a data management module, an optimization algorithm module, a decision-making process module, and a result output module.

[0005] The data management module is used to input, check, and store basic information such as warehouses, carriers and vehicles (including load / volume constraints), orders (including time windows and cargo dimensions), as well as cost accounting rules and carbon footprint parameter configuration items.

[0006] The optimization algorithm module integrates a multi-stage decision-making algorithm model. In the path planning algorithm section, it incorporates evolutionary algorithms, particularly combining the characteristics of genetic algorithms (GA) and local search. It finds the optimal solution to the problem by simulating natural selection and genetic mechanisms. In the 3D bin packing algorithm section, items are first stacked according to certain rules or strategies, forming multiple two-dimensional "stack layers." Items within each stack layer are tightly arranged vertically and satisfy constraints such as stability (larger items do not crush smaller ones). Then, these stack layers are treated as two-dimensional "items," and using methods for solving two-dimensional bin packing problems, they are packed into two-dimensional "containers" as efficiently as possible, thus achieving effective utilization of three-dimensional space.

[0007] The decision-making process module, based on existing information, constitutes key human-factor decisions. First, experienced dispatchers select a central warehouse for orders awaiting decision-making. Then, based on the current decision context and carrier information, they choose a carrier to handle the transportation and select several existing orders from the database. Next, they select an optimization target and initiate the optimization process. The optimization time varies depending on the order size. Once the optimization is complete, detailed information about the optimization plan can be queried, and the system automatically outputs reports on the allocated costs and carbon footprints for each order.

[0008] The output module displays route planning schemes in map visualization and tables, and packing schemes in 3D visualization and specific cargo locations. It also generates a comprehensive cost-carbon footprint report, supports blockchain ledger storage, and provides a data interface for supply and demand auction transactions.

[0009] The supply and demand auction module is based on GS stable matching and the auction mechanism of the 2020 Nobel laureate. Through multiple auctions combined with GS matching theory, each time the matched parties are filtered out. The supply and demand parties that have not reached a transaction publish the highest price to enter the next round until the current stage exit criteria are reached (all supply and demand are cleared, the maximum number of transactions is reached, or the maximum time limit is reached), thereby balancing the time and price sensitivity of the supply and demand parties and achieving efficient transaction matching. Beneficial effects

[0010] This system enables collaborative optimization of route planning and 3D packing, enhancing the supply flexibility of e-commerce to downstream customers, increasing loading rates while ensuring timely delivery, and reducing operating costs. Detailed Implementation

[0011] Scenario 1: Implementation of basic functions for path planning and 3D box packing System development is done in a software manner.

[0012] Users input information such as warehouse, order, and carrier, and select the warehouse, carrier, and order for optimization. The system calculates the route arrangement using optimization algorithms and finally visualizes the optimized solution. Taking an e-commerce logistics scenario as an example, users input warehouse, carrier, and order information, select optimization objectives, and the system calculates and outputs route planning and 3D packing solutions.

[0013] Scenario 2: Refined Management of Costs and Carbon Footprint When handling massive orders during shopping festivals like "618" and "Double 11," e-commerce logistics and distribution centers first use a route-packing collaborative optimization subsystem to plan vehicle delivery routes and cargo packing schemes. This effectively improves loading efficiency and reduces vehicle mileage compared to traditional methods. Simultaneously, a cost and carbon footprint tracking module collects real-time data on fuel consumption and electricity usage for each vehicle. Combined with preset carbon emission coefficients, it accurately calculates the delivery cost and carbon footprint for each order. For example, when a courier vehicle delivers a batch of goods, the system automatically calculates transportation costs and carbon emissions based on vehicle load and mileage. Costs and carbon footprints are then allocated proportionally to the cargo owner in the consortium blockchain ledger. The data is tamper-proof and fully traceable. The resulting carbon footprint report can be directly used for enterprise ESG disclosure and carbon trading confirmation.

[0014] Scenario 3: Intelligent supply and demand matching transactions The logistics capacity trading platform integrates resources from numerous small and medium-sized e-commerce businesses and third-party logistics companies. E-commerce sellers submit cargo transportation requests, including cargo dimensions, weight, origin and destination, and time requirements; logistics companies publish vehicle capacity information, covering vehicle models, remaining space, load capacity, price per kilometer, and carbon emission factors. The system uses an improved GS matching algorithm to initially screen trading pairs where supply and demand are roughly matched in space and time, entering a multi-round auction stage. Referring to the 2020 Nobel Prize auction theory, unmatched capacity providers and cargo owners successively announce their highest acceptable freight rate and lowest acceptable discount in each round of bidding. The system intelligently adjusts the median price, guiding both parties to gradually approach a transaction. For example, a logistics company initially quotes 2.5 yuan / km, while the e-commerce seller's target price is 2 yuan / km. After three rounds of auctions, the two parties reach a cooperation agreement at 2.2 yuan / km. After the transaction is completed, the system prioritizes pushing the route planning and packing scheme for this transportation task to the trading pair to ensure the transaction is executed. At the same time, the system writes the transaction record, cost sharing details, and carbon footprint data into the consortium blockchain, providing a basis for subsequent credit assessment and pricing strategy adjustments. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the present invention, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0016] Figure 1 Figure 2(a) shows the route and packing optimization task sheet, Figure 2(b) shows the delivery route map, Figure 2(c) shows the vehicle and container loading map, and Figure 2(d) shows the packing slice map. Figure 3 This is a diagram of the cost and carbon footprint tracking module architecture. Figure 4 This is a flowchart of the supply and demand matching transaction process.

Claims

1. A path planning and three-dimensional packing system, characterized in that, include: The data management module is used to input, inspect, and store warehouse, carrier and vehicle, order information, as well as cost and carbon footprint related parameters; The optimization algorithm module integrates a multi-stage decision-making algorithm model. It generates an initial path scheme through a genetic algorithm and optimizes the packing stack layer based on local search (satisfying the constraint that larger items do not crush smaller ones). After stacking items into layers, it performs three-dimensional packing using a two-dimensional packing method, while simultaneously evaluating cost and carbon emission benefits. The decision-making process module, based on existing information, allows users to interactively select optimization objectives (time priority or loading rate priority). The dispatch staff selects the warehouse, carrier, and order, determines the optimization objective, and initiates the optimization. The results output module displays route planning schemes with time windows in map visualization and tables, and packing schemes in 3D visualization and specific cargo locations. It also outputs a comprehensive cost-carbon footprint report and supports blockchain notarization and data interfaces. The supply and demand auction matching module, based on GS matching and the auction mechanism of the 2020 Nobel laureate, uses multiple auctions combined with GS matching theory. Each time, matched trading parties are filtered out, and the supply and demand parties that have not reached a transaction publish the highest price to enter the next round, until the current stage exit criteria are reached (all supply and demand are cleared, the maximum number of transactions is reached, or the maximum time limit is reached), thereby balancing the time and price sensitivity of supply and demand parties and achieving efficient transaction matching.

2. The path planning and three-dimensional packing system according to claim 1, characterized in that, The data management module allows users to input the following information and store it in the database: • Warehouse information (warehouse ID, warehouse address, etc.) • Carrier and vehicle information (carrier ID, carrier name, container type, container length, width and height, vehicle ID, maximum vehicle load capacity, maximum volume, etc.) • Order information (order ID, order address, length, width and height of the goods, delivery time, etc.), as well as cost and carbon footprint parameters such as vehicle energy consumption per unit mileage, carbon emission coefficient, and warehousing energy consumption.

3. Input data includes:

4. The path planning and three-dimensional packing system according to claim 1, characterized in that, The optimization algorithm module uses a combination of genetic algorithm and local search for path planning.

5. The path planning and three-dimensional packing system according to claim 1, characterized in that, The optimization algorithm module uses a combination of genetic algorithm and local search for path planning, while incorporating transportation costs and carbon emissions into the optimization objective function.

6. The path planning and three-dimensional packing system according to claim 1, characterized in that, The three-dimensional bin packing algorithm employs a layered bin packing strategy: The goods are stacked in a two-dimensional layer along the height direction; A greedy algorithm is used to fill the two-dimensional layer into the container cross-section, and the carbon emission benefits of different schemes can be evaluated simultaneously during the loading process.

7. The path planning and three-dimensional packing system according to claim 1, characterized in that, The decision-making process module allows dispatch staff to select warehouses, carriers, and orders, and determine optimization objectives.

8. The path planning and three-dimensional packing system according to claim 1, characterized in that, The output module includes map visualization and table display of the route planning scheme, as well as 3D visualization of the packing scheme and display of specific cargo locations. It also generates a comprehensive cost-carbon footprint report, supports blockchain ledger storage, and provides a data interface for supply and demand auction transactions.

9. The path planning and three-dimensional packing system according to claim 1, characterized in that, The supply and demand auction matching module is based on GS matching and the auction mechanism of the 2020 Nobel laureate. Through multiple auctions combined with GS matching theory, each time the matched parties are filtered out. The supply and demand parties that have not reached a transaction publish the highest price to enter the next round until the current stage exit criteria are reached (all supply and demand are cleared, the maximum number of transactions is reached, or the maximum time limit is reached), thereby balancing the time and price sensitivity of the supply and demand parties and achieving efficient transaction matching.

10. The output data is:

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