Complex Process Optimization Using Utility-Guided Element 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), face challenges due to exponential growth in search spaces, leading to impractical computation times and sub-optimal solutions when considering multiple factors beyond distance, like timeliness and resource constraints.
Innovation Solution
A computer-implemented method using the Minimum Insertion Heuristic (MIH) and Reinsertion Improvement Heuristic (RIH) to optimize the insertion order of elements into a solution, leveraging a full utility model to maximize marginal utility, allowing for efficient optimization of complex systems in various applications like scheduling, resource allocation, and routing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If brute force method is used to solve optimization problems, then optimal solution can be found, but computation time becomes impractical for large search spaces
Solution Approach 1:
The patent applies preliminary action by using the Minimum Insertion Heuristic to construct an initial solution before applying more sophisticated optimization techniques. This initial construction phase prepares the solution in a way that enables subsequent Reinsertion Improvement Heuristic to efficiently refine it, avoiding the need to explore the entire search space from scratch while still achieving near-optimal results.
Solution Approach 2:
The patent segments the optimization process into distinct phases: (1) initial solution construction using Minimum Insertion Heuristic, (2) solution improvement using Reinsertion Improvement Heuristic, and (3) iterative refinement through multiple passes. This segmentation allows each phase to focus on specific aspects of optimization, achieving good results without exhaustive search.
2Loss of time
If traditional heuristic algorithms like Lin-Kernighan are used, then computation time is reduced, but they only handle distance as cost and cannot accommodate multiple factors like timeliness and resource constraints
Solution Approach 1:
The patent implements universality by designing a heuristic framework that can handle multiple types of costs and constraints (distance, timeliness, resource availability, preferences) within a unified optimization model. The utility function accommodates various constraint types, making the algorithm versatile for different real-world scenarios beyond just distance-based TSP.
Solution Approach 2:
The patent applies parameter changes by allowing the cost function to incorporate multiple parameters (distance, time, resource constraints, preferences) rather than just distance. The utility function dynamically adjusts weights and parameters to reflect different optimization criteria, enabling the same algorithm to adapt to various problem formulations with different constraint types.
3Productivity
If Minimum Insertion Heuristic is used to build solution element by element, then the process is simple and fast, but the final solutions are significantly sub-optimal
Solution Approach 1:
The patent applies feedback by using the Reinsertion Improvement Heuristic to evaluate and refine the initial solution constructed by Minimum Insertion Heuristic. The RIH examines each element's position, calculates the utility impact of moving elements, and makes improvements based on this feedback. This iterative feedback loop transforms the sub-optimal MIH solution into a near-optimal solution while maintaining computational efficiency.
Solution Approach 2:
The patent uses preliminary action by having Minimum Insertion Heuristic construct an initial feasible solution that serves as a starting point for subsequent refinement. This preliminary construction phase creates a baseline solution that can be efficiently improved, avoiding the need to start from scratch while maintaining good solution quality.
4Adaptability or versatility
If meta-heuristic algorithms like Simulated Annealing or Genetic Algorithms are used, then generalized utility functions can be handled, but the search process is slow and local optimality is rarely guaranteed
Solution Approach 1:
The patent applies self-service by designing a deterministic heuristic algorithm that does not rely on randomization or external guidance mechanisms typical of meta-heuristics. The Minimum Insertion Heuristic and Reinsertion Improvement Heuristic autonomously construct and refine solutions based on utility calculations, eliminating the need for random search components while achieving competitive solution quality and speed.
Data Source
AI summary
A computer-implemented method for optimizing control of a process includes a computer receiving a process definition and a collection of elements to be arranged in a way that optimizes the utility of the process; initializing the process using a collection of discrete elements (stops, events, work orders, tasks, locations, etc.), and producing a solution for said collection by inserting the elements into the solution using the Minimum Insertion Heuristic; modifying the order of the elements to be inserted and continuing the process to produce solutions with better scores; and continuing to produce solutions based on improving the order of insertion until the process is determined to be sufficiently optimized.