Merge pull request #33 from LAMPSPUC/dev

Dev

Author: Giovanni3A

Project.toml

@@ -1,7 +1,7 @@
 name = "ApplicationDrivenLearning"
 uuid = "0856f1c8-ef17-4e14-9230-2773e47a789e"
-authors = ["Giovanni Amorin"]
-version = "0.1.0"
+authors = ["Giovanni Amorim", "Joaquim Garcia"]
+version = "0.1.1"
 
 [deps]
 BilevelJuMP = "485130c0-026e-11ea-0f1a-6992cd14145c"

src/ApplicationDrivenLearning.jl

@@ -135,15 +135,10 @@ and new constraint fixing to original forecast variables.
 function build_plan_model_forecast_params(model::Model)
     # adds parametrized forecast variables using MOI.Parameter
     forecast_size = size(model.forecast_vars)[1]
-    model.plan_forecast_params = @variable(
-        model.plan,
-        _forecast[1:forecast_size] in MOI.Parameter.(zeros(forecast_size))
-    )
-    # fixes old and new prediction variables together
+    model.plan_forecast_params = plan_forecast_vars(model)
     @constraint(
         model.plan,
-        plan_forecast_fix,
-        model.plan_forecast_params .== plan_forecast_vars(model)
+        model.plan_forecast_params .∈ MOI.Parameter.(zeros(forecast_size))
     )
 end
 

src/jump.jl

@@ -64,6 +64,20 @@ function JuMP.add_variable(
             name * "_assess",
         ),
     )
+
+    # forecast variables can't have bounds
+    if JuMP.has_lower_bound(forecast.plan)
+        @warn "Forecast variable lower bound will be removed."
+        JuMP.delete_lower_bound(forecast.plan)
+        JuMP.delete_lower_bound(forecast.assess)
+    end
+
+    if JuMP.has_upper_bound(forecast.plan)
+        @warn "Forecast variable upper bound will be removed."
+        JuMP.delete_upper_bound(forecast.plan)
+        JuMP.delete_upper_bound(forecast.assess)
+    end
+
     push!(model.forecast_vars, forecast)
     return forecast
 end

src/simulation.jl

@@ -3,17 +3,33 @@ function compute_single_step_cost(
     y::Vector{<:Real},
     yhat::Vector{<:Real},
 )
+    # set forecast params as prediction output
     MOI.set.(model.plan, POI.ParameterValue(), model.plan_forecast_params, yhat)
+    # optimize plan model
     optimize!(model.plan)
-    @assert termination_status(model.plan) == MOI.OPTIMAL "Optimization failed for PLAN model"
+    # check for solution and fix assess policy vars
+    try
+        set_normalized_rhs.(
+            model.assess[:assess_policy_fix],
+            value.(plan_policy_vars(model)),
+        )
+    catch e
+        println("Optimization failed for PLAN model.")
+        throw(e)
+    end
+    # fix assess forecast vars on observer values
     fix.(assess_forecast_vars(model), y; force = true)
-    set_normalized_rhs.(
-        model.assess[:assess_policy_fix],
-        value.(plan_policy_vars(model)),
-    )
+    # optimize assess model
     optimize!(model.assess)
-    @assert termination_status(model.assess) == MOI.OPTIMAL "Optimization failed for ASSESS model"
-    return objective_value(model.assess)
+    # check for optimization
+    try
+        return objective_value(model.assess)
+    catch e
+        println("Optimization failed for ASSESS model")
+        throw(e)
+    end
+    # should never get here
+    return 0
 end
 
 """