Automated Job Grouping for Batch Processing Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing batch processing systems face difficulties in efficiently grouping jobs due to their manual and intuitive nature, leading to challenges in tracking failures, delays, and taking corrective actions, especially with complex interdependencies and unique constraints, which existing graph theory solutions fail to adequately address.
Innovation Solution
A processor-implemented method and system that collects precedence relationships, business critical jobs, and constraints to create logically connected components, using domain-aware trimming and splitting approaches to derive smaller components that satisfy constraints, while ensuring all constraints are met and minimizing impact on Service Level Agreements (SLAs).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual and intuitive approaches are used for grouping jobs, then ease of operation is maintained, but productivity and reliability deteriorate due to difficulty in tracking failures and delays
Solution Approach 1:
The patent replaces manual and intuitive job grouping approaches with an automated system that uses graph theory algorithms. The system automatically models batch jobs as nodes and dependencies as edges in a graph, then applies algorithms to identify logical groups, thereby substituting human manual operations with automated computational mechanisms to improve productivity while maintaining ease of use through automated decision-making
Solution Approach 2:
The system enables self-service by automatically analyzing job dependencies, precedence relationships, and constraints to determine optimal groupings without human intervention. The automated system processes input data, applies algorithms, and generates logical groups independently, allowing the system to serve itself rather than requiring manual configuration or intuitive decision-making
2Productivity
If graph theory solutions are used to model batches as node-edge graphs, then productivity is improved through automated grouping, but device complexity increases due to multiple constraints and objective functions
Solution Approach 1:
The patent segments the complex job batching problem into manageable components by dividing jobs into logical groups based on dependencies and constraints. The system processes the graph in stages, identifying critical paths, then systematically assigning jobs to batches while satisfying multiple constraints, thereby breaking down the complex optimization problem into simpler, sequential steps that reduce overall system complexity
Solution Approach 2:
The system applies local quality by considering unique constraints and characteristics of individual jobs and dependencies when forming groups. Rather than applying a uniform grouping strategy, the system analyzes local properties of each job node and edge in the graph, adjusting grouping decisions to satisfy specific constraints such as execution time windows, resource requirements, and dependency relationships, thereby managing complexity through localized optimization
3Manufacturing precision
If multiple objective functions are incorporated into job grouping, then manufacturing precision is improved through better job coordination, but device complexity increases due to conflicts between constraints and objective functions
Solution Approach 1:
The patent applies preliminary action by pre-processing the job dependency graph to identify critical paths and precedence relationships before final grouping decisions are made. The system analyzes constraints and objective functions in advance, establishes a hierarchical structure for job grouping, and prepares multiple candidate solutions before selecting the final arrangement, thereby reducing complexity during the actual grouping process while maintaining precision in job coordination
Solution Approach 2:
The system implements dynamics by allowing flexible adjustment of groupings based on conflicting constraints and objective functions. Rather than rigidly enforcing a single grouping strategy, the system dynamically reconfigures job assignments to satisfy multiple objectives simultaneously, adapting the logical groups based on the specific constraints and priorities provided, thereby managing complexity through adaptive, dynamic optimization
Data Source
AI summary
Many of the existing approaches for grouping of jobs for processing are manual in nature, and are based on intuition. Other approaches involve modeling batches as node-edge graphs and use existing graph theory solutions such as connected components or cliques to find logical subgraphs. However, this approach isn't efficient due to the fact that there may be various constraints unique to batch systems. The disclosure herein provides a method and system for creating logical groups of jobs meeting objective function and satisfying constraints. In this method, a logical grouping of jobs is performed such that all defined constraints are satisfied. If any job is not satisfying one or more of the defined constraints, smaller components of the jobs are derived such that the smaller components satisfy the constraints. The logical groups are formed by merging the jobs and the smaller components satisfying the constraints.