jshbrmn/irt-service
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simplify this to the core level so we can easily reference it

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This commit is contained in:
Adrian Manteza 2022-03-10 22:49:26 +00:00
parent d6e1cb37ce
commit d8b4675f44
1 changed files with 14 additions and 84 deletions
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98
app/services/solver_sandbox.py
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@ -61,9 +61,8 @@ class SolverSandbox:
print(v.name, "=", v.varValue)
break
def yas_elastic(tif_targets = [50.0], tcc_targets = [50.0]):
Bundles = [1,2,3,4,5]
Items = [1,2,3,4,5]
def yas_elastic(tif_target = 50.0):
Items = [1,2,3,4,5]
# For TIF target
tif = {
@ -83,28 +82,9 @@ class SolverSandbox:
}
# ---
# For TCC target
trf = {
1: 100,
2: 200,
3: 400,
4: 600,
5: 800
}
irf = {
1: 100,
2: 200,
3: 300,
4: 500,
5: 700
}
# ---
total_forms = 2
items = LpVariable.dicts('Item', Items, cat='Binary')
bundles = LpVariable.dicts('Bundle', Bundles, cat='Binary')
items = LpVariable.dicts('Item', Items, cat='Binary')
for form in range(total_forms):
drift = 0
@ -115,68 +95,20 @@ class SolverSandbox:
problem = LpProblem('TIF_TCC', LpMinimize)
# objective function
problem += lpSum(
[
(iif[i] + irf[i]) * items[i] for i in Items
] +
[
(tif[b] + trf[b]) * bundles[b] for b in Bundles
]
), 'TIF_TCC_Sum'
problem += lpSum([(tif[i] + iif[i]) * items[i] for i in Items])
# Constraint 1
problem += lpSum([items[i] for i in Items]) == 3, 'TotalItems'
print(f"Calculating TIF target of {tif_target} with drift of {drift} for Form {form + 1}")
# Our own "Elastic Constraints"
problem += lpSum(
[
items[i] for i in Items
] +
[
bundles[b] for b in Bundles
]
) == 3, 'TotalItems'
for tif_target in tif_targets:
print(f"Calculating TIF target of {tif_target} with drift of {drift} for Form {form + 1}")
# Our own "Elastic Constraints"
problem += lpSum(
[
tif[b] * bundles[b] for b in Bundles
] +
[
iif[i] * items[i] for i in Items
]
) >= tif_target - (tif_target * drift_percent), 'TifIifMin'
problem += lpSum(
[
tif[b] * bundles[b] for b in Bundles
] +
[
iif[i] * items[i] for i in Items
]
) <= tif_target + (tif_target * drift_percent), 'TifIifMax'
for tcc_target in tcc_targets:
print(f"Calculating TCC target of {tcc_target} with drift of {drift} for Form {form + 1}")
# Our own "Elastic Constraints"
problem += lpSum(
[
trf[b] * bundles[b] for b in Bundles
] +
[
irf[i] * items[i] for i in Items
]
) >= tcc_target - (tcc_target * drift_percent), 'TrfIrffMin'
problem += lpSum(
[
trf[b] * bundles[b] for b in Bundles
] +
[
irf[i] * items[i] for i in Items
]
) <= tcc_target + (tcc_target * drift_percent), 'TrfIrfMax'
[(tif[i] + iif[i]) * items[i] for i in Items]
) >= tif_target - (tif_target * drift_percent), 'TifIifMin'
problem += lpSum(
[(tif[i] + iif[i]) * items[i] for i in Items]
) <= tif_target + (tif_target * drift_percent), 'TifIifMax'
problem.solve()
@ -191,8 +123,6 @@ class SolverSandbox:
drift += 1
# breakpoint() if drift == 10:
else:
print(f"solution found with drift of {drift}!")