Generalisering vekting av sekundærmål#4
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…ests for availability scenarios
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Trenger vi å ha med denne endringen? Hva er greien?
| # n^2*x + n*x + h, der n er antall intervjuer, x er en konstant. | ||
| def calculate_secondary_objective_weights(num_interviews: int) -> dict[str, float]: | ||
| # Vekten for clustering øker kvadratisk med antall intervjuer, for å prioritere det mer når det er mange intervjuer. | ||
| clustering_weight = 0.001 * (num_interviews ** 2) |
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Dette vil vel fort føre til at det prioriteres over antall intervjuer.
Du mener kanskje 1/( num_interviews ** 2)?
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Foreslår noe sånt som dette for å generalisere det:
secondary_penalties = [
[...], # listen av variabler for én type
[...] # listen av variabler for neste type
]
mip.xsum(mip.xsum(1/(num_interviews ** i)) * penalities for i, penalties in enumerate(secondary_penalties))
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