Scheduling

ortidy.scheduling.shift_scheduling.shift_scheduling(requirements, workers, *, day_column='day', shift_column='shift', required_column='required', worker_id_column='workerId', min_shifts=None, max_shifts=None, time_limit=None, random_seed=0)[source]

Build a balanced shift roster.

Parameters:

  • requirements (Any) – Frame of (day, shift, required) — how many workers each shift on each day needs.

  • workers (Any) – Frame with a worker-id column.

  • day_column (str) – Day column within requirements.

  • shift_column (str) – Shift column within requirements.

  • required_column (str) – Required-headcount column within requirements.

  • worker_id_column (str) – Worker-id column within workers (and the output).

  • min_shifts (int | None) – Optional minimum number of shifts per worker.

  • max_shifts (int | None) – Optional maximum number of shifts per worker.

  • time_limit (float | None) – Optional wall-clock limit in seconds.

  • random_seed (int) – Solver seed for determinism.

Returns:

SolveResult whose frame (same backend as workers) has one row per assigned (workerId, day, shift). The objective is the maximum number of shifts assigned to any single worker (minimized for fairness).

Return type:

SolveResult

ortidy.scheduling.job_shop.job_shop(tasks, *, job_column='jobId', step_column='step', machine_column='machine', duration_column='duration', start_column='start', end_column='end', time_limit=None, random_seed=0)[source]

Solve a job-shop scheduling problem.

Parameters:

  • tasks (Any) – Tidy frame of tasks with job, step (order within the job), machine, and duration columns.

  • job_column (str) – Input column names.

  • step_column (str) – Input column names.

  • machine_column (str) – Input column names.

  • duration_column (str) – Input column names.

  • start_column (str) – Names of the added schedule columns.

  • end_column (str) – Names of the added schedule columns.

  • time_limit (float | None) – Optional wall-clock limit in seconds.

  • random_seed (int) – Solver seed for determinism.

Returns:

SolveResult whose frame is the input frame (same backend) plus start and end columns; objective is the makespan.

Return type:

SolveResult