Schedule Optimization System Construction Support via Template-Based Source Code Generation
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Solution Overview
Problem
Existing schedule optimization systems face challenges in developing solutions for complex optimization problems within practical time frames, especially when the number of determination variables is large and constraint conditions are complicated, and they struggle with reducing development man-hours when few past assets are available.
Innovation Solution
A schedule optimization system construction support device that generates source code by inserting optimization problem information and scheduled data into a template, allowing for the development of systems capable of solving complex optimization problems without relying on past assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a general-purpose allocation program library is used to reduce development man-hours, then development efficiency is improved, but the system cannot easily solve complicated optimization problems with large numbers of determination variables
Solution Approach 1:
The system segments the schedule optimization problem into multiple determination variables and constraint conditions, allowing the general-purpose allocation program to handle each segment independently through systematic combination and evaluation, thereby maintaining both development efficiency and capability to solve complicated problems
Solution Approach 2:
The general-purpose allocation program is designed with multi-functionality to handle various types of determination variables and constraint conditions through a unified framework, enabling it to solve different complicated optimization problems without requiring problem-specific customization
2Ease of manufacture
If past assets are reused to reduce development man-hours, then development efficiency is improved, but the system cannot be developed when few or no past assets are available
Solution Approach 1:
The system employs self-service mechanisms where the general-purpose allocation program automatically generates determination variables, constraint conditions, and evaluation criteria based on the input schedule data, eliminating the need for past assets while maintaining high development efficiency
Solution Approach 2:
The system performs preliminary action by pre-defining a comprehensive framework of determination variables and constraint conditions that can be directly applied to any schedule optimization problem, allowing immediate development without relying on historical assets
3Adaptability or versatility
If the number of determination variables is increased to handle complex schedules, then the system can solve more complex problems, but the calculation time is increased
Solution Approach 1:
The system applies partial action by evaluating determination variables in prioritized batches rather than all at once, processing the most critical variables first to achieve satisfactory solutions without exhaustively evaluating every possible combination, thereby reducing calculation time while maintaining capability to handle complex schedules
Solution Approach 2:
The system uses periodic action by implementing iterative evaluation cycles that reassess determination variables in phases, allowing early termination when satisfactory solutions are found and enabling efficient handling of complex schedules through structured periodic review rather than continuous exhaustive search
Data Source
AI summary
A schedule optimization system capable of easily solving a complicated optimization problem without using past assets is developed.A storage unit 120 includes an optimization problem description table indicating an optimization problem for drafting a schedule in a schedule optimization system 300, a scheduled data table indicating a value of a constant being a fixed element in the optimization problem, and a template as a pattern of a source code of the schedule optimization system. A processing unit 130 generates the source code of the schedule optimization system by inserting information based on the optimization problem description table and the scheduled data table into the template.