Field Workforce Dispatching Using Knowledge Graphs and MILP
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Solution Overview
Problem
Existing field workforce management systems face challenges in efficiently dispatching personnel to off-site locations due to factors such as available personnel, task constraints, and target response times, leading to ineffective workforce management.
Innovation Solution
A computer-implemented method using integer programming to generate a directed graph, extended knowledge graph, and mixed-integer linear program (MILP) to optimize the dispatching of workers, considering customer information, resource and budget constraints, and operational rules, with an MILP solver engine generating solutions for managing workers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional workforce management methods are used, then implementation is simpler, but scheduling efficiency and optimization are insufficient
Solution Approach 1:
The patent introduces an intermediary processing layer that transforms complex workforce scheduling data into a structured format suitable for MILP optimization. This intermediary layer includes data preprocessing modules that clean and standardize input data, and model generation modules that translate business rules into mathematical constraints, thereby enabling efficient optimization without requiring direct complex system architecture
Solution Approach 2:
The patent segments the workforce management system into distinct functional modules: data collection module, data preprocessing module, MILP model generation module, solver execution module, and result analysis module. Each module handles specific tasks independently, improving overall scheduling efficiency while maintaining manageable system complexity through modular design
2Measurement precision
If complex constraints and rules are modeled in detail, then solution accuracy improves, but computational resources required increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing workforce data before optimization, including validating data formats, standardizing time representations, and pre-calculating basic metrics. This preliminary preparation ensures that the MILP solver receives clean, structured data, improving solution accuracy while reducing the computational burden during the actual optimization process
Solution Approach 2:
The patent transforms complex workforce constraints into standardized mathematical parameters suitable for MILP formulation. By converting qualitative business rules into quantitative constraints with specific parameter formats, the system achieves precise modeling of complex scenarios while maintaining computational efficiency through parameter standardization
3Reliability
If more detailed customer information and resource constraints are considered, then dispatching quality improves, but problem complexity increases
Solution Approach 1:
The patent creates a universal MILP modeling framework that can handle multiple types of constraints and business rules through a unified mathematical structure. This framework uses standardized constraint templates that can accommodate various customer information requirements and resource constraints, improving dispatching quality across different scenarios while avoiding the need for separate complex models for each case
Data Source
AI summary
A computer-implemented method of generating a directed graph associated with a set of workers, a set of customers, and a set of tasks associated with the set of customers, based on: customer information associated with the set of tasks; and resource and budget information associated with the set of customers is provided. Aspects include generating an extended knowledge graph based on the directed graph and a set of operation and business rules. Aspects include generating, based on the extended knowledge graph, a mixed-integer linear program (MILP) problem associated with completing the set of tasks. Aspects include generating, by a MILP solver engine, one or more solutions associated with dispatching and managing the set of workers in association with solving the MILP problem.