Reinforcement Learning Module for Vehicle Loading Optimization
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Solution Overview
Problem
The computational complexity of allocating storage space and selecting the right type and number of transportation vehicles for goods is excessively high, leading to inefficient solutions that often exceed processing time and memory constraints due to the NP-hard nature of these problems.
Innovation Solution
A reinforcement learning module is employed to efficiently solve vehicle selection and space allocation problems by simulating loading scenarios, learning policies through rewards and penalties, and optimizing vehicle and space utilization within specified time and resource constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional computational methods are used to solve vehicle selection and space allocation problems, then solution accuracy can be achieved, but computational time and memory usage become excessively high
Solution Approach 1:
The patent segments the complex vehicle selection and space allocation problem into multiple sub-problems handled by separate neural network models. Each model specializes in a specific aspect (vehicle selection, space allocation, loading sequence), allowing parallel processing and reducing overall computational time while maintaining solution accuracy through coordinated optimization.
Solution Approach 2:
The patent employs preliminary action by pre-training specialized neural network models offline on historical data and problem configurations. These pre-trained models can then rapidly infer solutions for new problems without requiring extensive real-time computation, significantly reducing operational computational time while preserving accuracy.
2Reliability
If exhaustive search methods are used to evaluate all possible combinations of enclosures and vehicles, then optimal solutions can be found, but memory requirements exceed available resources
Solution Approach 1:
The patent uses copying by training neural network models on copies of historical problem data and configurations during the offline training phase. The models learn from these data copies to generalize solution patterns, eliminating the need to store and process all possible problem combinations in memory during real-time operation.
Solution Approach 2:
The patent applies parameter changes by transforming the discrete combinatorial optimization problem into a continuous parameter optimization problem suitable for neural network processing. This transformation changes the problem representation from enumerating all combinations to optimizing continuous parameters that define vehicle selections and space allocations, dramatically reducing memory requirements.
3Productivity
If multiple vehicle types and enclosure configurations are considered to improve solution quality, then better resource utilization is achieved, but problem complexity increases exponentially
Solution Approach 1:
The patent segments the complex multi-factor optimization problem into distinct functional modules, each handled by a specialized neural network. This segmentation allows the system to consider multiple vehicle types and enclosure configurations without the complexity exploding, as each module processes its specific aspect independently and efficiently.
Solution Approach 2:
The patent implements universality by designing a multi-functional neural network architecture that can handle various vehicle types, enclosure configurations, and problem variations within a single unified framework. This universal approach avoids the need for separate complex algorithms for each scenario, maintaining manageable system complexity while achieving high resource utilization.
Data Source
AI summary
A method and system are provided where a module employing reinforcement learning (RL) can learn to solve the vehicle selection and space allocation problems for the transportation and/or storage of goods. In one embodiment, a method includes: (a) obtaining a specification of a load that includes enclosures of one or more enclosure types, and the specification includes, for each enclosure type: (i) dimensions of an enclosure of the enclosure type, and (ii) a number of enclosures of the enclosure type. The method also includes (b) obtaining a specification of vehicles of one or more vehicle types, where the specification includes, for each vehicle type: (i) dimensions of space available within a vehicle of the vehicle type, and (ii) a number of vehicles of the vehicle type that are available for transportation. The method further includes (c) providing a simulation environment for simulating loading of a vehicle. In addition, the method includes (d) selecting by an agent module a vehicle of a particular type, where: the selected vehicle has space available to accommodate at least a portion of the load; and the selection is based on a state of the environment, an observation of the environment, and a reward received previously from the environment; (e) receiving by the agent module a current reward from the environment in response to selecting the vehicle; and (f) repeating by the agent module steps (d) and (e) until the load is accommodated within space available in the one or more selected vehicles.