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tilearn.bs.basis

Core helpers for building and enriching job tables.

Job table schema​

The baseline row format is:

[name, p, r, d, w]

Some functions append additional columns in-place.

tilearn.list_gen(rows, cols)​

Create a 2D zero-initialized matrix with shape (rows, cols).

tilearn.job_amount(a)​

Return the number of jobs (rows) in a.

tilearn.p_factor_nonprec(i, a)​

Return the non-precedence priority factor for row i, computed as w / p.

tilearn.p_factor_prec_tu(i, a) and tilearn.p_factor_prec(...)​

Compatibility helpers for precedence experiments and cumulative normalization.

note

The precedence helpers are retained for backward compatibility and are not the core path for standard non-precedence workflows.

tilearn.calculate_factor(a)​

Compute all w / p factors without mutating a.

Returns a one-column matrix.

tilearn.factor_data(a)​

Append one factor column (w / p) to each row in-place.

tilearn.c_time(a)​

Append completion time C_j based on current row order.

tilearn.lateness(a)​

Append lateness L_j = C_j - d_j.

Call c_time(a) first when starting from base rows.

tilearn.sum_factor(a)​

Append cumulative ratio:

(w_1 + ... + w_j) / (p_1 + ... + p_j)

Used by precedence-oriented list ranking.

Example workflow​

import tilearn as tl

jobs = [
    ["J1", 2.0, 0, 8, 4.0],
    ["J2", 1.0, 0, 5, 1.0],
]

jobs = tl.factor_data(jobs)  # append w/p at index 5
jobs = tl.c_time(jobs)       # append C_j
jobs = tl.lateness(jobs)     # append L_j
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