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Pass GHA pipeline and clean up GHA files #4

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merged 3 commits into from
Jul 4, 2023
Merged

Pass GHA pipeline and clean up GHA files #4

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03f07f1
Create python-app.yml
vahidnourbakhsh Jul 4, 2023
b331c87
delete workflows dir
vahidnourbakhsh Jul 4, 2023
cb67843
format black
vahidnourbakhsh Jul 4, 2023
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0 workflows/python-app.yml → .github/workflows/main.yml
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46 changes: 39 additions & 7 deletions examples/dea/dea.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,19 @@
from pyomo.environ import AbstractModel, Set, Param, Var, Objective, Constraint, PositiveReals, NonNegativeReals, Binary, maximize, inequality, SolverFactory
from pyomo.environ import (
AbstractModel,
Set,
Param,
Var,
Objective,
Constraint,
PositiveReals,
NonNegativeReals,
Binary,
maximize,
inequality,
SolverFactory,
)

TOLERANCE = 0.01 # feasibility tolerance for the normalization constraint below
TOLERANCE = 0.01 # feasibility tolerance for the normalization constraint below

model = AbstractModel()
# Sets
Expand All @@ -17,18 +30,37 @@
model.u = Var(model.Outputs, within=NonNegativeReals)
model.v = Var(model.Inputs, within=NonNegativeReals)


# Objective
def efficiency_rule(model):
return sum(model.outvalues[j, unit]*model.target[unit]*model.u[j] for unit in model.Units for j in model.Outputs)
return sum(
model.outvalues[j, unit] * model.target[unit] * model.u[j]
for unit in model.Units
for j in model.Outputs
)


model.efficiency = Objective(rule=efficiency_rule, sense=maximize)


# Constraints
def ratio_rule(model, unit):
value = sum(model.outvalues[j, unit]*model.u[j] for j in model.Outputs) - sum(model.invalues[i, unit]*model.v[i] for i in model.Inputs)
value = sum(model.outvalues[j, unit] * model.u[j] for j in model.Outputs) - sum(
model.invalues[i, unit] * model.v[i] for i in model.Inputs
)
return inequality(body=value, upper=0)


model.ratio = Constraint(model.Units, rule=ratio_rule)


def normalization_rule(model):
value = sum(model.invalues[i, unit]*model.target[unit]*model.v[i] for unit in model.Units for i in model.Inputs)
return inequality(body=value, lower=1-TOLERANCE, upper=1 + TOLERANCE)
model.normalization = Constraint(rule=normalization_rule)
value = sum(
model.invalues[i, unit] * model.target[unit] * model.v[i]
for unit in model.Units
for i in model.Inputs
)
return inequality(body=value, lower=1 - TOLERANCE, upper=1 + TOLERANCE)


model.normalization = Constraint(rule=normalization_rule)
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