Solves a given assignment problem with: (a) the data supplied; and (b) those data perturbed by a Gaussian distribution (μ, σ) = (1, 'perturbation'). (This perturbation should be well below ~33 %, to avoid negative random data.)

If the given matrix is Random, it is an arbitrary Gaussian (1, 0.1).

Files involved: P.energy.php (this one), Energy.php, energyAP.py.

Draws plots of the optima, and of the triangular distribution.

Keywords: Energy; assignment; Monte Carlo; sensitivity; PHP; Python; gnuplot.

• (Wikipedia) PHP (from Personal Home Page); Assignment Problem

• NIST/SEMATECH e-Handbook of Statistical Methods, 1.3.5.11. Measures of Skewness and Kurtosis, accessed Dec-2024. Negative (pos.) skewness: long left (right) tail. High (low) kurtosis: heavy (light) tails.

• Python, random, accessed May-2026. (Period, 2**19937-1 = 4.3e+6001 .)

• eNergetics, 12.th Virtual International Conference on Science, Technology and Management in Energy, Belgrade (Serbia), 23–24 November 2026.

• 1819-08-30: Serret, Joseph Alfred (†1885-03-02, 65 yrs.).