Constraint Programming for Request Resource Assignment
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Solution Overview
Problem
Existing methods for assigning requests to resources in enterprises are inefficient, leading to unsatisfactory completion of requests due to timely, skill, and customer requirement issues.
Innovation Solution
A data model is created using constraint programming that includes constraints and resource capabilities to optimize the assignment of requests to resources, employing a sorting and filtering process to select the most suitable resources for each request.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional request assignment methods are used, then the system is simpler to operate, but request completion timeliness and resource matching accuracy deteriorate
Solution Approach 1:
The patent replaces traditional manual or simple automated assignment mechanisms with constraint programming technology. The system uses constraint satisfaction problems (CSPs) to model request-resource assignment, where constraints represent resource capabilities, request requirements, and assignment rules. This substitution enables intelligent optimization of resource matching while maintaining system manageability through automated constraint processing.
Solution Approach 2:
The patent transforms the assignment problem by changing parameters into a formal constraint programming framework. Request attributes and resource capabilities are converted into constraints with specific domains and values. The system dynamically adjusts constraint priorities and weights to optimize assignment decisions, enabling timely response while managing complexity through parameterized constraint handling.
2Measurement precision
If resource capabilities are thoroughly evaluated for each request, then resource matching accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing resource capability information into a structured constraint framework. Resource capabilities are pre-modeled as constraints with defined domains, and request requirements are pre-translated into constraint conditions. This preparation enables rapid matching during actual assignment without re-evaluating all capabilities from scratch, thus maintaining accuracy while reducing processing time.
Solution Approach 2:
The patent segments the resource evaluation process into distinct constraint components. Each resource capability is represented as a separate constraint with specific attributes (domain, range, priority). The constraint programming solver then efficiently combines these segmented constraints to find optimal assignments, avoiding the need to re-evaluate all capabilities for each request and significantly reducing computational overhead.
3Reliability
If multiple constraints are applied to ensure satisfactory request completion, then request quality improves, but the difficulty of detecting and measuring assignment effectiveness increases
Solution Approach 1:
The patent implements feedback mechanisms that monitor constraint satisfaction during and after assignment. The system tracks which constraints are satisfied by each assignment decision and provides feedback on assignment effectiveness. This feedback loop enables continuous improvement of constraint modeling and allows measurement of satisfaction levels against predefined constraints, making effectiveness detectable and measurable.
Data Source
AI summary
A data model for application to a constraint programming solver is generated. The data model includes a set of data model elements. A particular data model element corresponds to a particular request. The particular data model element also corresponds to one or more resources that may be assigned to the request. The data model also includes a set of constraints. One or more sort/search algorithms may be applied with the data model to the constraint programming solver. The sort/search algorithms may direct the constraint programming solver to output certain preferred assignments of resources to requests.