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make it a single target calculation for yas_elastic

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Adrian Manteza 2022-03-11 15:24:57 +00:00
parent 26571cffc6
commit e1176365f3
1 changed files with 34 additions and 36 deletions
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70
app/services/solver_sandbox.py
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@ -61,7 +61,7 @@ class SolverSandbox:
print(v.name, "=", v.varValue)
break
def yas_elastic(tif_target = 50.0):
def yas_elastic(tif_target = 140.0): # 140 is the optimal
Items = [1,2,3,4,5]
# For TIF target
@ -82,57 +82,55 @@ class SolverSandbox:
}
# ---
total_forms = 2
items = LpVariable.dicts('Item', Items, cat='Binary')
for form in range(total_forms):
drift = 0
max_drift = 10 # 10% elasticity
drift = 0
max_drift = 10 # 10% elasticity
while drift <= max_drift:
drift_percent = drift / 100
problem = LpProblem('TIF_TCC', LpMinimize)
while drift <= max_drift:
drift_percent = drift / 100
problem = LpProblem('TIF_TCC', LpMinimize)
# objective function
problem += lpSum([(tif[i] + iif[i]) * items[i] for i in Items])
# objective function
problem += lpSum([(tif[i] + iif[i]) * items[i] for i in Items])
# Constraint 1
problem += lpSum([items[i] for i in Items]) == 3, 'TotalItems'
# 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}")
print(f"Calculating TIF target of {tif_target} with drift of {drift}%")
# Our own "Elastic Constraints"
problem += lpSum(
[(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'
# Our own "Elastic Constraints"
problem += lpSum(
[(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()
problem.solve()
if LpStatus[problem.status] == 'Infeasible':
print(f"attempt infeasible for drift of {drift}")
if LpStatus[problem.status] == 'Infeasible':
print(f"attempt infeasible for drift of {drift}")
for v in problem.variables():
print(v.name, "=", v.varValue)
for v in problem.variables():
print(v.name, "=", v.varValue)
print(problem.constraints)
print(problem.objective)
print(problem.objective.value())
print(problem.constraints)
print(problem.objective)
drift += 1
drift += 1
else:
print(f"solution found with drift of {drift}!")
else:
print(f"solution found with drift of {drift}!")
for v in problem.variables():
print(v.name, "=", v.varValue)
for v in problem.variables():
print(v.name, "=", v.varValue)
print(problem.constraints)
print(problem.objective)
print(problem.constraints)
print(problem.objective)
break
break
# Implementation of the Whiskas Cat problem, with elastic constraints
# https://www.coin-or.org/PuLP/CaseStudies/a_blending_problem.html