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Fix a quantity that is data in one model and a decision in another#

Write one spec in which a quantity, such as a plant's size, is chosen by the solver in one study and given by the data in another. The file does not change between the two. The data does.

  1. Declare the quantity as a variable, with named bounds.
dimensions:
  plant: { dtype: str }
parameters:
  size_min: { dims: [plant] }
  size_max: { dims: [plant] }
variables:
  size:
    dims: [plant]
    bounds: { lower: size_min, upper: size_max }

  1. Write every rule against the variable. rate - relmax * size <= 0 is one equation whether size is chosen or given.

  2. Pin it in the data where it is given. Attach size_min and size_max as the same value for a plant whose size is fixed. Equal bounds pin a variable (variables).

A pinned variable is still a variable: size * on is variable * variable, and size cannot stand in another variable's bounds:. Where a bound has to come from it, ship the column as a parameter too.

Fix it in the spec instead#

Where a driver decides the quantity and the model it solves must not, call spec.fix. A Benders subproblem, a myopic step and a rolling window each do this:

subproblem = spec.fix('size')

size becomes a parameter under the same name, so every expression goes on reading it. Its bounds become the assumption size_within_bounds. A constraint that named only size becomes an assumption too, because it now compares numbers. Where size has a where: and absence: undefined, each row that reads it outside a sum has to stand under that mask: fix adds the mask to a constraint's where:, or refuses and names the reader.