pyomo

问题 I am trying to model an Electric Vehicle scheduling to minimise the electricity bill. Of course, the EV will only be able to charge every time that is plugged into a charging station (i.e. it cannnot charge when the vehicle is driving). Basically, it is a stationary battery that can be disconnected and connected again. I already managed to model a stationary battery scheduling before and is working good as I expected, however I am having a hard time setting up a constraint to connect and

问题 I have some Pyomo code (see below) which produces one possible solution to the linear equation: 3a + 2b + 2c = 15 Where: -20 <= a <= 0 0 <= b <= 20 20 <= c <= 50 a, b, c are real numbers How could I rewrite the program to produce a random solution every time it is run? I am open to using Pulp as well. My Code: # Coefficients coeffs = [3,2,2] y_target = 15 # Define model and variables model = ConcreteModel() model.a = Var(bounds=(-20,0)) model.b = Var(bounds=(0,20)) model.c = Var(bounds=(20,50

问题 I am setting up a biggish energy optimization problem with pyomo. The setup took unreasonably long as mentioned in other questions, however I managed to speed up most of the problematic lines, except the energy flow constraints. Opposed to all other constraints the flow includes a sum over all elements. So I decided to rewrite the way the flow variables are created so they include an index over all elements, hoping this would improve the situation. My code now looks like this: def flows(model

问题 I am setting up a biggish energy optimization problem with pyomo. The setup took unreasonably long as mentioned in other questions, however I managed to speed up most of the problematic lines, except the energy flow constraints. Opposed to all other constraints the flow includes a sum over all elements. So I decided to rewrite the way the flow variables are created so they include an index over all elements, hoping this would improve the situation. My code now looks like this: def flows(model

问题 I have a Pyomo model that has a sparse set of values but I get the error Cannot index a component with an indexed set when I try to index a binary variable according to this sparse set. For a simplified example: model = ConcreteModel() model.S = Set([1, 4, 6]) model.V = Var(model.S, within=Binary) 回答1: The line model.S = Set([1, 4, 6]) creates an Indexed Set: that is a Set of 3 Sets, each one of which is empty (Pyomo treats positional arguments as indexing sets - just like in your comment

问题 I have a Pyomo model that has a sparse set of values but I get the error Cannot index a component with an indexed set when I try to index a binary variable according to this sparse set. For a simplified example: model = ConcreteModel() model.S = Set([1, 4, 6]) model.V = Var(model.S, within=Binary) 回答1: The line model.S = Set([1, 4, 6]) creates an Indexed Set: that is a Set of 3 Sets, each one of which is empty (Pyomo treats positional arguments as indexing sets - just like in your comment

问题 I have a Pyomo model that has a sparse set of values but I get the error Cannot index a component with an indexed set when I try to index a binary variable according to this sparse set. For a simplified example: model = ConcreteModel() model.S = Set([1, 4, 6]) model.V = Var(model.S, within=Binary) 回答1: The line model.S = Set([1, 4, 6]) creates an Indexed Set: that is a Set of 3 Sets, each one of which is empty (Pyomo treats positional arguments as indexing sets - just like in your comment

问题 I have a question regarding to pyomo dual variable retrieval. I'm trying to retrieve the dual variable from the constraint "model.market_clearing_const". However, after running these codes folowing, the error I got is: "Component with id '2886755528520': market_clearing_const[0]" So basically if I look into the dual component under model, I see suffix=0. So this means there's no dual variables are stored in suffix despite the fact that I already declare it. And my solution is feasible. Any

问题 This code works correctly. def m(model, i): return model.fd_amt[i] <= getattr(model, 'br_sa_ind')[i] * global_m setattr(model, ind+"_m",Constraint(model.br_standalone_I, rule=m)) But this def m(model, i, ind_name): return model.fd_amt[i] <= getattr(model, ind_name)[i] * global_m setattr(model, ind+"_m",Constraint(rule=m(model,model.model.br_standalone_I, 'br_sa_ind') )) results in this error: ERROR: evaluating expression: No value for uninitialized NumericValue object fd_amt[br_standalone_I]

问题 I'd like to find the optimal solution for buying goods from suppliers where the shipping cost is dependent on the cost of goods bought from given supplier. I'm using Pyomo. My code so far is: model = ConcreteModel(name="(MN_2)") # products N = ['prod1', 'prod2', 'prod3'] # suppliers M = ['A', 'B'] # price p = {('prod1', 'A'): 10, ('prod2', 'A'): 9, ('prod3', 'A'): 50, ('prod1', 'B'): 16, ('prod2', 'B'): 20, ('prod3', 'B'): 35} # user quantity contraint q_u = {('prod1', 'A'): 2, ('prod2', 'A')