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Krzysztof kuhy Rudnicki
ffa5dad02a
The 250-line cap applies to prose too, and the reports were the worst
offenders -- ENUME's projectA.tex alone was 1,876 lines. Each is now a
preamble plus one \input{} per section, which is how a long LaTeX document is
normally organised; not one byte of prose was rewritten.
"The PDF is unchanged" is checked by a script rather than by eye, because
hashing the PDF cannot work: pdflatex stamps a /CreationDate and an /ID into
every build, so two runs of the SAME source already differ. tex_fingerprint.sh
compares what a reader sees instead -- page count plus a hash of the extracted
text -- and it is stable across rebuilds of untouched sources, which is the
property that makes it usable as a gate.
It earned its keep immediately. Both ECOTE reports quote an example LaTeX
document inside an lstlisting block, so \end{document} appears in the middle
of the file; splitting on the FIRST one stranded everything after it. Same
page count, different text, caught automatically. EOPSY lab3 exposed a second
bug: a \section{} title that runs onto the next line.
Result: 19 .tex over the cap -> 1. The survivor,
ELAC/.../03-building-nfa-...tex, is 418 lines of consecutive tikz figures
with no subsection to split on and needs a hand-placed boundary.
Verified: 29 splits across two passes (section, then subsection for the
pieces still over), every one reporting `unchanged` from
scripts/tex_fingerprint.sh --check.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0172dj34FDpao6wsix8ZuCzE
105 lines
3.6 KiB
TeX
105 lines
3.6 KiB
TeX
\section{Implementacja}
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Do implementacji użyty został python z biblioteką pulp \href{https://coin-or.github.io/pulp/index.html}{https://coin-or.github.io/pulp/index.html} \\
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Dzięki temu wykorzystujemy zarówno łatwość pythona jak i możliwości używania różnych solverów (CBC, GLPK, CPLEX, Gurobi...) przez pulpa \\
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\begin{lstlisting}[language=Python, caption={Import bilbioteki pulp i bibliotek używanych do wykresu}]
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import pulp
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import sys
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\end{lstlisting}
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\begin{lstlisting}[language=Python, caption={Iniicjalizacja modelu}]
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model = pulp.LpProblem("Optimal_Distribution", pulp.LpMinimize)
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\end{lstlisting}
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\begin{lstlisting}[language=Python, caption={Zdefiniowanie zmiennych i parametrów}]
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fabryki = ['F1', 'F2']
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magazyny = ['M1', 'M2', 'M3', 'M4']
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dostawcy = fabryki + magazyny
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klienci = ['K1', 'K2', 'K3', 'K4', 'K5', 'K6']
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koszt = {
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'F1': {'M1': 0.3, 'M2': 0.5, 'M3': 1.2, 'M4': 0.8,
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'K1': 1.2, 'K2': 999, K3': 1.2, 'K4': 2.0,
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'K5': 999, 'K6': 1.1},
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'F2': {'M1': 999, 'M2': 0.4, 'M3': 0.5, 'M4': 0.3,
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'K1': 1.8, 'K2': 999, 'K3': 999, 'K4': 999,
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'K5': 999, 'K6': 999},
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'M1': {'K1': 999, 'K2': 1.2, 'K3': 0.2, 'K4': 1.7,
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'K5': 999, 'K6': 2.0},
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'M2': {'K1': 1.4, 'K2': 0.3, 'K3': 1.8, 'K4': 1.3,
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'K5': 0.5, 'K6': 999},
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'M3': {'K1': 999, 'K2': 1.3, 'K3': 2.0, 'K4': 999,
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'K5': 0.3, 'K6': 1.4},
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'M4': {'K1': 999, 'K2': 999, 'K3': 0.4, 'K4': 2.0,
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'K5': 0.5, 'K6': 1.6}
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}
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P = pulp.LpVariable.dicts(
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"P", [(i, j) for i in dostawcy for j in klienci],
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cat='Binary')
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maksymalne_zamowienie = 60
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poziom_satysfakcji = {
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'K1': 50 / maksymalne_zamowienie,
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'K2': 10 / maksymalne_zamowienie,
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'K3': 40 / maksymalne_zamowienie,
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'K4': 35 / maksymalne_zamowienie,
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'K5': 60 / maksymalne_zamowienie,
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'K6': 20 / maksymalne_zamowienie}
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preferencja_klienta = {
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'K1': ['F2'], 'K2': ['M1'], 'K3': ['M2', 'M3'],
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'K4': ['F1'], 'K5': [], 'K6': ['M3', 'M4']}
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suma_satysfakcji = pulp.lpSum(
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[poziom_satysfakcji[j] * P[(i, j)]
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for i in dostawcy for j in klienci])
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\end{lstlisting}
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\begin{lstlisting}[language=Python, caption={Zmienne decyzyjne}]
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x = pulp.LpVariable.dicts(
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"x",
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[(i, j) for i in dostawcy for j in klienci],
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lowBound=0, cat='Integer')
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y = pulp.LpVariable.dicts("y",
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[(i, k) for i in fabryki for k in magazyny],
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lowBound=0, cat='Integer')
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\end{lstlisting}
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\begin{lstlisting}[language=Python, caption={Funkcje celu}]
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koszt_dystrybucji = pulp.lpSum(
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[koszt[i][j] * x[(i, j)]
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for i in dostawcy for j in klienci])
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koszt_magazynowania = pulp.lpSum(
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[koszt[i][k] * y[(i, k)]
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for i in fabryki for k in magazyny])
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alpha = 0.5
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beta = 0.5
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model += alpha
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* (koszt_dystrybucji + koszt_magazynowania)
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- beta * suma_satysfakcji
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\end{lstlisting}
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\begin{lstlisting}[language=Python, caption={Ograniczenia}]
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for i in fabryki:
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model += pulp.lpSum(
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[x[(i, j)] for j in klienci]
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+ [y[(i, k)] for k in magazyny])
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<= mozliwosci_fabryki[i]
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for k in magazyny:
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model += pulp.lpSum(
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[x[(k, j)] for j in klienci]
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) <= pojemnosc_magazynu[k]
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for j in klienci:
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model += pulp.lpSum(
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[x[(i, j)] for i in dostawcy]) == zamowienia_klientow[j]
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\end{lstlisting}
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\begin{lstlisting}[language=Python, caption={Rozwiązanie problemu}]
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model.solve()
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\end{lstlisting}
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\
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\begin{lstlisting}[language=Python, caption={Przedstawienie wyników}]
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for v in model.variables():
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print(v.name, "=", v.varValue)
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\end{lstlisting}
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