Print Job Scheduling Using Answer Set Programming and Historical Data
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Solution Overview
Problem
Complex job scheduling problems in printing workflows, which involve both quantitative and qualitative aspects, are challenging for traditional programming techniques due to their instability and the need for consistent performance, especially in industrial applications.
Innovation Solution
A method using an Answer Set Programming (ASP) solver with a historical decision database to determine print job schedules, where the database stores successful decision frequencies for scheduling classifications, enabling faster and more consistent performance by guiding the solver through previously successful search branches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional programming techniques (C, C++, Java) are used to solve complex job scheduling problems in printing workflows, then the system can handle quantitative and qualitative constraints, but the programming becomes complex, difficult to write, debug, and maintain
Solution Approach 1:
The patent replaces traditional imperative programming mechanics (C, C++, Java) with a declarative programming paradigm (Answer Set Programming). Instead of manually coding algorithms to search through scheduling alternatives, the system uses ASP solvers that automatically compute solutions from high-level problem descriptions. This substitution reduces program complexity while maintaining the ability to handle complex quantitative and qualitative scheduling constraints through declarative rule specifications.
Solution Approach 2:
The patent changes the fundamental parameter of programming abstraction from low-level imperative commands to high-level declarative specifications. By using ASP, programmers describe what the solution should satisfy (constraints and objectives) rather than how to compute it step-by-step. This parameter change in abstraction level dramatically reduces complexity for complex scheduling problems while preserving adaptability to various constraints.
2Ease of operation
If ASP solvers are used to solve scheduling problems, then programming abstraction is improved, but performance becomes unstable with inconsistent solving times
Solution Approach 1:
The patent applies preliminary action by performing classification of scheduling problems into distinct categories (e.g., based on problem characteristics, constraints, or difficulty) before solving. The system pre-processes the ASP problem to identify its type, then selects or configures appropriate solving strategies, heuristics, or parameters in advance. This preliminary classification enables the system to optimize solver configuration for each problem type, ensuring consistent and reliable performance while maintaining high programming abstraction.
Solution Approach 2:
The patent implements feedback mechanisms where the ASP solver's performance is monitored and analyzed. Based on feedback from previous solving attempts and problem characteristics, the system dynamically adjusts solver parameters, selects appropriate solving strategies, or reconfigures the problem representation. This feedback loop ensures that performance remains consistent and reliable across different scheduling problems while preserving the benefits of high-level declarative programming.
3Loss of time
If high-level declarative languages are used, then solution development time is reduced, but solving performance may be slower compared to optimized low-level programs
Solution Approach 1:
The patent performs preliminary optimization by classifying ASP problems and pre-configuring solving strategies before execution. The system analyzes problem characteristics in advance and selects optimal solver configurations, heuristics, and search strategies tailored to each problem type. This preliminary action bridges the performance gap between high-level declarative languages and optimized low-level programs, maintaining fast solving speeds while preserving the rapid development benefits of ASP.
Solution Approach 2:
The patent dynamically changes solver parameters based on problem characteristics. Instead of using fixed solver configurations, the system adjusts computational parameters, search strategies, and resource allocation based on the specific scheduling problem being solved. This parameter adaptation enables the ASP solver to achieve solving speeds comparable to optimized low-level programs while maintaining the productivity advantages of high-level declarative programming.
Data Source
AI summary
A method for determining a print job schedule for a printing production facility having a set of availably printing resources, comprising: defining one or more scheduling classifications; receiving one or more print jobs, each print job having a print job description specified by a set of print job attributes; determining one or more scheduling classification corresponding to the received print jobs; using a processor to automatically determine the print job schedule for the received print jobs using an answer set programming language solver responsive to the print job descriptions, a set of resource descriptions, a set of scheduling rules, and a historical decision database.