Decision

Note

This section of the documentation is auto-generated from the code of the Julia-based core model. Refer to IESopt.jl for any further details (which may require some familiarity with Julia).

If you spot incorrect math-mode rendering, or similar issues, please file an issue, since rendering documentation from Julia to Python is not the easiest task.

Overview

A Decision represents a basic decision variable in the model that can be used as input for various other core component’s settings, as well as have associated costs.

Parameters

lb

Minimum size of the decision value (considered for each “unit” if count allows multiple “units”).

  • mandatory: no

  • default: \(0\)

  • values: numeric

  • unit: -

ub

Maximum size of the decision value (considered for each “unit” if count allows multiple “units”).

  • mandatory: no

  • default: \(+\infty\)

  • values: numeric

  • unit: -

cost

Cost that the decision value induces, given as cost \cdot value.

  • mandatory: no

  • default: \(0\)

  • values: numeric

  • unit: monetary (per value)

fixed_value

If mode: fixed, this value is used as the fixed value of the decision. This can be useful if this Decision was used in a previous optimization and its value should be fixed to that value in the next optimization (applying it where ever it is used, instead of needing to find all usages). Furthermore, this allows extracting the dual value of the constraint that fixes the value, assisting in approaches like Benders decomposition. Note that this does not change the induced cost in any way.

  • mandatory: no

  • default: \(-\)

  • values: numeric

  • unit: -

fixed_cost

This setting activates a “fixed cost” component for this decision variable, which requires that the model’s problem type allows for binary variables (e.g., MILP). This can be used to model fixed costs that are only incurred if the decision variable is active (e.g., a fixed cost for an investment that is only incurred if the investment is made). If the decision is 0, no fixed costs have to be paid; however, if the decision is greater than 0, the fixed cost is incurred. Note that after deciding to activate the decision, the overall value is still determined in the usual (continuous) way, incuring the (variable) cost as well. More complex cost functions can be modelled by switching to mode sos1 or sos2 and using the sos parameter.

  • mandatory: no

  • default: \(-\)

  • values: -

  • unit: monetary

mode

Type of the decision variable that is constructed. linear results in a continuous decision, integer results in a integer variable, binary constrains it to be either 0 or 1. sos1 and sos2 can be used to activate SOS1 or SOS2 mode (used for piecewise linear costs). See fixed_value if setting this to fixed.

  • mandatory: no

  • default: \(linear\)

  • values: linear, binary, integer, sos1, sos2, fixed

  • unit: -

sos

TODO (meanwhile, refer to the SOS or PiecewiseLinearCost example).

  • mandatory: no

  • default: \(-\)

  • values: list

  • unit: -

build_priority

Priority for the build order of components. Components with higher build_priority are built before. This can be useful for addons, that connect multiple components and rely on specific components being initialized before others.

  • mandatory: no

  • default: \(1000\)

  • values: numeric

  • unit: -

Detailed reference

Expressions

Variables

fixed

How to access this variable?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_decision").var.fixed

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_decision").var.fixed

Full implementation and all details: decision/var_fixed @ IESopt.jl

to be added

sos

How to access this variable?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_decision").var.sos

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_decision").var.sos

Full implementation and all details: decision/var_sos @ IESopt.jl

to be added

value

How to access this variable?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_decision").var.value

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_decision").var.value

Full implementation and all details: decision/var_value @ IESopt.jl

Add the variable describing the value of this decision to the model. If lower and upper bounds (decision.lb and decision.ub) are the same, the variable will immediately be fixed to that value. This can be accessed via decision.var.value.

Constraints

fixed

How to access this constraint?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_decision").con.fixed

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_decision").con.fixed

Full implementation and all details: decision/con_fixed @ IESopt.jl

to be added

sos1

How to access this constraint?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_decision").con.sos1

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_decision").con.sos1

Full implementation and all details: decision/con_sos1 @ IESopt.jl

to be added

sos2

How to access this constraint?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_decision").con.sos2

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_decision").con.sos2

Full implementation and all details: decision/con_sos2 @ IESopt.jl

to be added

sos_value

How to access this constraint?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_decision").con.sos_value

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_decision").con.sos_value

Full implementation and all details: decision/con_sos_value @ IESopt.jl

to be added

Objectives

fixed

How to access this objective?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_decision").obj.fixed

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_decision").obj.fixed

Full implementation and all details: decision/obj_fixed @ IESopt.jl

to be added ```

sos

How to access this objective?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_decision").obj.sos

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_decision").obj.sos

Full implementation and all details: decision/obj_sos @ IESopt.jl

Add the cost defined by the SOS-based value of this Decision to the model.

value

How to access this objective?

# Using Julia (`IESopt.jl`):
import IESopt

model = IESopt.run(...)  # assuming this is your model
IESopt.get_component(model, "your_decision").obj.value

# Using Python (`iesopt`):
import iesopt

model = iesopt.run(...)  # assuming this is your model
model.get_component("your_decision").obj.value

Full implementation and all details: decision/obj_value @ IESopt.jl

Add the cost defined by the value of this Decision to the model:

 

\[ \text{value} \cdot \text{cost} \]