Dynamic Process Sequencing via Normalized Experience Values
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Solution Overview
Problem
Existing methods for sequencing process options in data processing systems and theme park scenarios fail to consider the experiences of other members, leading to inefficient use of resources and potential delays or bad experiences, as they are prior-experience agnostic.
Innovation Solution
The method involves measuring and normalizing experience values for each member using activation functions to dynamically sequence process options based on past experiences, allowing or preventing the inclusion of process options in a sequence based on trigger thresholds, thereby optimizing the overall experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional queuing methods (FIFO, priority-based) are used to sequence process options, then the sequencing mechanism is simple and easy to implement, but the system cannot utilize past experiences to optimize sequencing, leading to inefficient resource use and potential delays
Solution Approach 1:
The system performs preliminary actions by measuring and normalizing experience values for each member before sequencing decisions are made. Experience values are measured for each member's interaction with process options, normalized to a common scale, and stored for future reference. This preliminary experience data collection enables the system to make informed sequencing decisions that avoid delays and improve resource utilization, resolving the contradiction between simple queuing and optimized sequencing.
Solution Approach 2:
The system implements feedback mechanisms by continuously measuring experience values from member interactions with process options, normalizing these values, and using them to dynamically adjust future sequencing decisions. The normalized experience values provide feedback about what works well and what causes delays, allowing the system to learn from past performance and optimize resource allocation over time, thereby improving productivity while managing complexity through systematic feedback loops.
2Loss of time
If experience-based dynamic sequencing is implemented to optimize resource allocation, then resource utilization improves, but the system complexity increases due to experience measurement and normalization requirements
Solution Approach 1:
The system performs preliminary actions by measuring and normalizing experience values for each member before sequencing decisions are made. Experience values are measured for each member's interaction with process options, normalized to a common scale, and stored for future reference. This preliminary experience data collection enables the system to make informed sequencing decisions that avoid delays and improve resource utilization, resolving the contradiction between simple queuing and optimized sequencing.
Solution Approach 2:
The system applies parameter changes by transforming raw experience values into normalized experience values through a normalization process. This parameter transformation converts diverse, hard-to-compare metrics into a standardized format that can be directly used for sequencing decisions. The normalization parameter change simplifies the complexity of comparing different types of experience data while enabling the system to identify patterns and make optimal sequencing choices that reduce processing delays.
3Ease of operation
If experience values are measured and normalized for all members to dynamically adjust sequencing, then member satisfaction improves, but the computational overhead and system complexity increase
Solution Approach 1:
The system performs preliminary actions by measuring and normalizing experience values for each member before sequencing decisions are made. Experience values are measured for each member's interaction with process options, normalized to a common scale, and stored for future reference. This preliminary experience data collection enables the system to make informed sequencing decisions that avoid delays and improve resource utilization, resolving the contradiction between simple queuing and optimized sequencing.
Solution Approach 2:
The system implements feedback mechanisms by continuously measuring experience values from member interactions with process options, normalizing these values, and using them to dynamically adjust future sequencing decisions. The normalized experience values provide feedback about what works well and what causes delays, allowing the system to learn from past performance and optimize resource allocation over time, thereby improving productivity while managing complexity through systematic feedback loops.
Data Source
AI summary
For experience-based dynamic sequencing of a set of process options, a first process option is executed at a first stage in a first sequence for a first set of members. The sequence includes several stages of executing a subset of the process options. An experience value is determined corresponding to executing the first process option. The experience value is normalized to calculate a normalized experience value corresponding to the first process option. Using the normalized experience value in a later stage from the plurality of stages, an evaluation is made whether a first trigger threshold is satisfied in a first activation function of a second process choice. When the first trigger threshold of the first activation function being satisfied, the second process choice is included in the first sequence at the later stage.