Incremental Shift Scheduling via MIP Solver Segmentation
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Solution Overview
Problem
Current methods for scheduling workers to cover demand are inefficient and fail to effectively model the complexity of shift design and assignment, often resulting in suboptimal solutions and inability to handle incremental changes without significant disruption to existing schedules.
Innovation Solution
A system utilizing a Mixed Integer Programming (MIP) solver to generate an incremental solution for shift design and assignment, which includes determining an updated set of shift candidates, calculating a new cost function, and restarting the solver to minimize disruptions, while respecting hard constraints and soft constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing scheduling approaches are used to model shift design and assignment, then the problem complexity can be represented, but the solution quality is suboptimal and cannot handle realistic size problems
Solution Approach 1:
The patent segments the shift scheduling problem into two distinct components: shift design (determining shift patterns and templates) and shift assignment (assigning workers to specific shifts). This segmentation allows each component to be optimized separately using appropriate algorithms, improving overall solution quality while handling larger problem sizes that would be intractable as a monolithic problem.
Solution Approach 2:
The patent introduces shift templates as an intermediary layer between demand requirements and worker assignments. These templates serve as standardized shift patterns that can be reused across multiple assignments, reducing the complexity of the optimization problem while maintaining high solution quality through systematic template selection and assignment.
2Adaptability or versatility
If manual scheduling is performed, then flexibility to consider multiple objectives can be achieved, but it consumes a great deal of time and is inefficient
Solution Approach 1:
The patent implements an automated iterative optimization process that incorporates feedback loops to evaluate multiple objectives (labor cost, worker preferences, coverage requirements) and adjust shift designs and assignments accordingly. This automated feedback mechanism replicates the flexibility of manual scheduling while eliminating the time consumption, systematically evaluating and re-evaluating solutions until optimal or near-optimal results are achieved.
3Manufacturing precision
If a complete rescheduling is performed when input data changes, then an optimized solution can be obtained, but large disruption to the existing schedule occurs
Solution Approach 1:
The patent performs preliminary analysis of the input data changes to identify only the affected portions of the schedule that require reoptimization. By pre-identifying the scope of changes and limiting the rescheduling effort to only those specific areas, the system maintains overall schedule stability while still achieving optimization where needed, avoiding the disruption of complete rescheduling.
Solution Approach 2:
The patent applies local quality optimization by focusing computational resources on optimizing only the local portions of the schedule that are affected by data changes, rather than uniformly reoptimizing the entire schedule. This approach maintains the stability of unaffected schedule portions while improving the quality of affected portions, reducing overall disruption.
Data Source
AI summary
A system for incremental solution of the shift design and assignment problem comprises an interface configured to receive an incremental change and an existing schedule. The system comprises a processor to determine whether labor demand has changed; in response to labor demand having been changed, generate an updated set of shift candidates; determine a new cost function; restart a solver using the updated set of shift candidates, the existing schedule, the incremental change, and the new cost function, wherein the solver comprises a mixed integer programming (MIP) solver, and wherein the MIP solver determines simultaneously a subset of the shift candidates selected in the final schedule and a set of shift assignments of which worker is assigned to which selected shift candidate of the subset of shift candidates.