Network Path Sequencing Using Utility-Guided Task Insertion
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Solution Overview
Problem
Current optimization methods for complex systems, such as the Traveling Salesman Problem (TSP) and Vehicle Routing Problem (VRP), are inefficient due to exponential growth in search spaces, making it impractical to consider all combinations, especially with modern computers, and existing heuristic algorithms like the Minimum Insertion Heuristic (MIH) and Reinsertion Improvement Heuristic (RIH) often result in sub-optimal solutions.
Innovation Solution
A computer-implemented method that uses a combination of the Minimum Insertion Heuristic and Reinsertion Improvement Heuristic to optimize the insertion order of elements into a solution, leveraging a utility model to maximize marginal utility, and applying meta-heuristic algorithms like Simulated Annealing or Genetic Algorithms to improve solution quality, while accounting for temporal, locational, and resource constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute force method is used to consider all possible solutions, then optimal solution can be found, but computation time becomes prohibitively long for large search spaces
Solution Approach 1:
The patent segments the search space by organizing elements into insertion orders and using heuristics to divide the problem into manageable subsets. Instead of evaluating all possible solutions simultaneously, the method breaks down the search into sequential insertion steps, where elements are added one by one in optimized orders, reducing the computational burden while maintaining solution quality.
Solution Approach 2:
The patent applies preliminary action by pre-determining insertion orders using heuristics before the actual optimization process. The Minimum Insertion Heuristic and Reinsertion Improvement Heuristic are used to pre-organize elements in sequences that are likely to lead to optimal solutions, avoiding the need to explore all possible permutations during the main optimization phase.
2Productivity
If Minimum Insertion Heuristic is used to build solution element by element, then computation time is reduced, but solution quality becomes significantly sub-optimal
Solution Approach 1:
The patent implements feedback by using the Reinsertion Improvement Heuristic to evaluate and improve the solution after the initial construction by the Minimum Insertion Heuristic. The system continuously assesses the solution quality and makes adjustments by reinserting elements in different positions, using the utility function feedback to guide improvements and escape local optima.
Solution Approach 2:
The patent applies dynamics by making the insertion order flexible and adaptive rather than static. The system dynamically adjusts the insertion sequence based on the utility function evaluation, allowing elements to be reinserted and repositioned during the optimization process. This dynamic approach enables the solution to evolve from an initial sub-optimal state to a near-optimal state.
3Measurement precision
If existing heuristic algorithms are applied to TSP, then distance optimization is achieved, but applicability to real-world problems with multiple constraints is limited
Solution Approach 1:
The patent achieves universality by designing a general-purpose optimization framework that can handle multiple types of constraints and objectives simultaneously. The utility function is designed to be configurable and can incorporate various factors such as distance, time, resource constraints, and business rules, making the system applicable to diverse real-world problems beyond just the classic TSP.
Solution Approach 2:
The patent applies parameter changes by allowing the utility function to dynamically adjust weights and parameters based on different problem instances and constraints. The system can modify the importance of different factors (e.g., prioritizing time over distance in some scenarios) to adapt to specific real-world requirements, enhancing versatility while maintaining optimization capability.
Data Source
AI summary
A system optimizes data transmission along network paths includes a communications interface; a processor, and a non-transient computer-readable storage medium having machine instructions for path optimizing and sequencing data transmission. The processor is configured to receive a model of a utility of data transmission in the network, receive a job that includes a sequence of data transmission tasks that optimize the utility of the data transmission; determine that a sequencing process represents an intractable problem; identify a best task insertion order for inserting tasks into a sequence, retaining as a current best task insertion order, a task insertion order that produces a current best score of a general utility function, until a stopping condition is reached; and using the current best task insertion order, apply the tasks to optimize the sequence of tasks of the job.