meetgrid

Meetgrid is a concept and software approach for organizing group meetings by mapping participant availability and meeting resources onto a two-dimensional grid of time slots and locations. The grid representation aims to streamline scheduling decisions by making feasible combinations immediately visible and comparable.

Overview and purpose

Meetgrid treats scheduling as an optimization problem. It aggregates availability data from calendars, considers time zones and room requirements, and then identifies time-resource cells that satisfy constraints such as attendee availability, room capacity, equipment needs, and organizational rules. The goal is to minimize back-and-forth messaging and to propose top meeting options that maximize attendance and convenience.

Data model and representation

Core entities include Participant, Availability, Meeting, TimeSlot, Room, and Grid. Availability links to calendar data, while TimeSlot defines a specific date and time window. Rooms carry capacity and features, enabling constraint checks for equipment, accessibility, and location. The Grid combines TimeSlots with Rooms to form potential meeting cells that can be evaluated against constraints.

Algorithms and workflow

Meetgrid relies on constraint satisfaction and optimization techniques, including linear or integer programming and heuristic methods. The typical workflow involves defining the meeting with participants and constraints, importing availabilities, generating the grid, running the solver, and presenting ranked options. Upon selection, invites are issued and resources are reserved.

Features and use cases

Key features include grid visualization, automatic slot recommendations, conflict detection, calendar integration, room booking, notifications, and audit trails. Use cases span corporate team scheduling, academic committees, and event planning where multiple attendees and resources must be coordinated efficiently.

Limitations

The effectiveness of meetgrid depends on data quality and calendar access. Privacy considerations and the complexity of large participant sets can pose challenges, requiring appropriate controls and robust data handling.