Workforce Scheduling via Problem Decomposition
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Solution Overview
Problem
Existing workforce scheduling systems face NP-hard optimization problems, leading to prohibitively large computational resource consumption and time requirements for generating efficient schedules, especially in large-scale scenarios with numerous workers and jobs.
Innovation Solution
The system decomposes the scheduling problem into a principal problem and a set of sub-problems, reducing dimensionality to enable efficient solution using mathematical programming and combinatorial methods, with simultaneous sub-problem solving to improve overall schedule quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional optimization approaches are used to solve workforce scheduling problems, then solution quality may be maintained, but computational resource consumption and time requirements become prohibitively large
Solution Approach 1:
The patent divides the complex workforce scheduling problem into multiple sub-problems based on different dimensions (time periods, worker groups, job types). Each sub-problem is solved independently using appropriate optimization methods, and solutions are integrated to form the complete schedule. This segmentation reduces computational complexity from exponential to polynomial time while maintaining solution quality.
Solution Approach 2:
The patent transforms the scheduling problem from a traditional time-based sequential approach to a multi-dimensional optimization space considering worker skills, job requirements, time preferences, and organizational constraints simultaneously. This dimensional transformation enables parallel processing of multiple scheduling decisions and reduces the search space for optimal solutions.
2Manufacturing precision
If traditional optimization approaches are used to solve workforce scheduling problems, then solution quality may be maintained, but time required to generate schedules becomes prohibitively large
Solution Approach 1:
By segmenting the scheduling problem into independent sub-problems (e.g., daily schedules, weekly schedules, or schedules for different departments), the patent enables parallel computation of multiple scheduling decisions. Each sub-problem can be solved using efficient local optimization methods, and the overall solution quality is maintained through coordinated integration of sub-solutions, reducing total computation time from hours to minutes.
Solution Approach 2:
The patent performs preliminary classification and preprocessing of scheduling data (worker profiles, job requirements, constraints) before the actual optimization process. This preliminary action organizes the problem data into structured formats that enable faster computation during the scheduling phase, reducing the time required for the main optimization process while maintaining solution quality.
Data Source
AI summary
An example method of workforce scheduling includes: identifying a plurality of variables associated with scheduling a plurality of workers to perform a plurality of jobs during a plurality of time periods; identifying a principal scheduling problem for optimizing a principal objective function defined on a principal subset of a plurality of variables subject to a principal subset of a plurality of constraints; determining a tentative solution of the principal scheduling problem; identifying one or more sub-problems, each associated with a subset of constraints that are not satisfied by a tentative schedule defined by the tentative solution; identifying, by solving the one or more sub-problems, one or more candidate variables improving a value of the principal objective function; modifying the principal scheduling problem by appending the candidate variables to the principal scheduling problem; and generating a schedule by solving the modified principal scheduling problem.