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Optimization & scheduling

Put the right people on the right work, every time the data changes

ByteWits combines operations research with AI to optimize staff scheduling and resource allocation. The engine can scan for new data on a regular cycle (every 5 minutes, for example) and recommend an updated plan for managers to approve.

  • Staff scheduling
  • Resource allocation
  • Capacity planning
  • Continuous rescans

The engine

Many inputs, one balanced recommendation

Employee availability
Skills
Capacity
Client demand
Project deadlines
Workload
Existing assignments
Budget constraints
Business rules
AI + OR optimization engine next scan in 05:00 Constraints · objectives · alternatives
Recommended scheduleAssignments by person and week, with the reason for each change
Conflicts flaggedIssues the engine could not resolve within your rules
Utilization outlookWho is over, under, or at target in the weeks ahead

Interactive example

Compare the current plan with the optimized one

Eight people, six weeks, the October 15 extension deadline in week two. Switch views to see how the same hours are redistributed and which constraints are met.

Staff utilization by week

Last data scan just now · next scan in 05:00

Demo data
  • ▽Under 70%
  • ✓70–100%
  • ▲Over 100%

Constraint check

    Continuous optimization

    What happens in each scan cycle

    The interval is configurable. Five minutes is a common example for scheduling data that changes during the day.

    1. Scan

      Read the latest data from scheduling, time, HR and project systems.

    2. Detect changes

      New PTO, a deadline moved, a new engagement, hours logged over budget.

    3. Re-solve

      Re-run the optimization with updated constraints, starting from the current plan.

    4. Compare

      Measure the new plan against the current one: utilization, risk, cost, continuity.

    5. Recommend

      Surface meaningful changes for approval. Minor changes can follow rules you set.

    Operations research, plainly

    Why optimization, not just AI

    Scheduling has hard rules: no one works 140% of their hours, required skills are non-negotiable, budgets have limits. Operations research is the field of mathematics built for exactly these problems.

    AI adds value around the edges: interpreting unstructured inputs, predicting demand, and explaining recommendations in plain language.

    Constraints

    Rules every plan must satisfy, such as availability, capacity and required skills.

    Objectives

    What “better” means to you: balanced workload, lower cost, fewer handoffs, deadline safety.

    Solver techniques

    Examples include linear and integer programming, constraint programming and heuristics. We choose per problem.

    Human review

    Managers approve, adjust or override, and the next scan starts from the approved plan.

    Capacity planning

    Look beyond this month's schedule

    The same engine feeds capacity planning: combine the forecast with current staffing to see when you'll run short and by how much.

    See the forecasting demo

    • Demand vs. capacity by month

      Where forecast demand exceeds the hours your team can deliver.

    • Skill-level gaps

      Not just “we need more hours” but which roles and skills are short.

    • Hiring and reallocation options

      Compare hiring, reallocating across offices, or seasonal staff.

    Scheduling still done in spreadsheets?

    Show us how you plan today. We'll show you what an optimized version could look like with your own data.

    Talk to an optimization expert