Utility-Driven Insertion Heuristics for Complex Process Optimization
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Solution Overview
Problem
Current methods for solving optimization problems like the Traveling Salesman Problem (TSP) and Vehicle Routing Problem (VRP) are inefficient due to exponential growth in search spaces, making it impossible to consider all combinations, even with modern computers, and existing heuristic algorithms often result in sub-optimal solutions.
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, with a utility function that maximizes marginal utility, allowing for improved solutions in complex systems such as scheduling, resource allocation, and routing problems.
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 computational time becomes prohibitively long for large search spaces
Solution Approach 1:
The patent segments the search space by introducing a hierarchical structure with multiple levels. Instead of evaluating all possible solutions at once, the algorithm divides the search into manageable segments using a tree-based approach where solutions are built incrementally level by level, allowing efficient exploration of large search spaces without exhaustive enumeration
Solution Approach 2:
The patent applies preliminary action by pre-calculating and storing utility function values for partial solutions at each level of the search tree. This allows the algorithm to make informed decisions at each step without having to re-evaluate all previous choices, significantly reducing computational time while maintaining solution optimality
2Productivity
If existing heuristic algorithms are used, then computational time is reduced, but solution quality becomes sub-optimal
Solution Approach 1:
The patent implements feedback mechanisms where the utility function values calculated at each level guide subsequent search decisions. The algorithm uses this feedback to prune branches that cannot lead to optimal solutions and to focus computational resources on promising areas of the search space, thereby maintaining high solution quality while improving efficiency
Solution Approach 2:
The patent changes the approach from using simple distance metrics to a comprehensive utility function that incorporates multiple parameters including distance, time windows, service requirements, and resource constraints. This parameter transformation allows the algorithm to evaluate solutions based on multiple criteria simultaneously, achieving both computational efficiency and solution optimality for complex real-world problems
3Measurement precision
If TSP algorithms are applied to general optimization problems, then distance optimization is achieved, but applicability to real-world problems with multiple constraints is limited
Solution Approach 1:
The patent creates a universal optimization framework that can handle multiple types of constraints and objectives through a unified utility function. The system is designed to be multi-functional, accommodating various problem types including routing, scheduling, and resource allocation by simply adjusting the utility function parameters, thereby achieving both distance optimization and broad problem applicability
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 the 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.