Office Space Scheduling With Team-Based Allocation Constraints
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Solution Overview
Problem
Current office space allocation tools fail to account for dynamic scheduling variables and organizational structure changes, leading to inefficient space utilization and employee experience.
Innovation Solution
An optimizer system that dynamically allocates office space based on enterprise organizational structure, employee teams, and business adjacencies, using a mixed-integer linear programming algorithm to optimize space occupancy and employee scheduling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed space allocation tools are used, then space assignment is simple, but dynamic scheduling variables and organizational structure changes are not accounted for
Solution Approach 1:
The patent implements dynamic space allocation by allowing the system to automatically adjust space assignments based on real-time organizational structure changes, team formations, and scheduling requirements. The optimization algorithm continuously adapts space assignments to match current business needs rather than using static fixed allocations.
Solution Approach 2:
The system changes key parameters such as space capacity, assignment duration, and team composition dynamically. The optimization algorithm processes varying parameters including team size, duration requirements, and organizational hierarchy to generate updated space assignments that reflect current operational needs.
2Productivity
If dynamic space allocation is implemented, then space utilization is optimized, but computational complexity increases
Solution Approach 1:
The patent segments the office space into distinct zones with specific capacities and characteristics. The optimization algorithm processes these segmented zones independently while considering overall constraints, which reduces computational complexity compared to treating the entire office as a single unit.
Solution Approach 2:
The system incorporates feedback mechanisms where space assignment results are fed back into the optimization algorithm to refine future assignments. This iterative feedback process improves space utilization efficiency while managing computational complexity through progressive optimization.
3Manufacturing precision
If multiple optimization controls are added, then scheduling precision is improved, but system complexity increases
Solution Approach 1:
The optimization controls are designed to serve multiple functions simultaneously. A single control parameter can influence team grouping, scheduling timing, and space allocation decisions, reducing the need for separate specialized controls and thereby managing overall system complexity while maintaining high scheduling precision.
Data Source
AI summary
A computer-implemented method is provided for scheduling employees' attendance to an office and allocating office space to accommodate the employees. The method includes assigning each of the employees to a plurality of employee teams based on enterprise information received. The method also includes allowing a user to adjust multiple optimization controls including one or more team group controls, scheduling controls and space controls. Each of the optimization controls represents at least one of a constraint that must be satisfied or a constraint satisfaction of which is optional. The method further including executing an optimization algorithm to simultaneously schedule the employee teams' attendance to the office over one or more periods of time while allocating a plurality of office space zones to the employee teams during the one or more periods of time. The optimization algorithm receives as inputs information about the employee teams, office space data and the optimization controls.