pyomo

问题 I'm trying to bring this constraint in my pyomo model [ 1 I define a set for indexing over time and I want to optimize the corresponding energy variable below model.grid_time = Set(initialize=range(0, 23))) model.charging_energy = Var(model.grid_time, initialize=0) My constraint definition looks like as follows: model.limits = ConstraintList() for t in model.grid_time: model.limits.add(sum(model.charging_energy[t] for t in model.grid >= energy_demand.at[t,"total_energy_demand"]) The problem

问题 I am writing a pyomo integer programme with a constraint of the form: def example_rule(model, j, t): value = sum(model.x[j,i]*(util[i][t]) for i in model.F) return 0 <= value <= 1 model.onelateral = Constraint(model.L, model.T, rule=example_rule) util[i][t] is a dict containing values that are always 0 or 1. model.x[j,i] is the binary decision variable. Sometimes when I run my model, it works fine. However, sometimes when I change the dimensions/values within util[i][t] it throws this error:

问题 Any example for multi-objective optimization in Pyomo? I am trying to minimize 4 Objectives (Non Linear) and I would like to use pyomo and ipopt. Have also access to Gurobi. I want to see even very simple example where we try to optimize for two or more objective (one minimization and one maximization) for a list of decision variables (not just one dimension but maybe a vector). Pyomo book that I have (https://link.springer.com/content/pdf/10.1007%2F978-3-319-58821-6.pdf) does not provide a

问题 Given inputs such as electricity consumption, generation from solar panel, price, (All at a given time t), we have a battery, and we want to evaluate how much it should (dis)/charge at any given time. The Problem can be formulated as follows: Pt = price of electricity at time t Lt = consumption of electricity at time t Zt = charge of battery at time t (how much is in the battery) St = Electricity generated from solar generator at time t Qt = amount the battery (dis)/charges at time t the

问题 Given inputs such as electricity consumption, generation from solar panel, price, (All at a given time t), we have a battery, and we want to evaluate how much it should (dis)/charge at any given time. The Problem can be formulated as follows: Pt = price of electricity at time t Lt = consumption of electricity at time t Zt = charge of battery at time t (how much is in the battery) St = Electricity generated from solar generator at time t Qt = amount the battery (dis)/charges at time t the

问题 I want to solve a constrained NLP in Pyomo. However, the objective function includes a normal distribution where the argument of the normal distribution contains the decision variable, e.g. an expression like - (norm.cdf(model.x[0])) . But I receive this error: Cannot convert object of type 'ndarray' (value = 1) to a numeric value. Is there a way of working with normal distributions in Pyomo? 回答1: I think that you can't use the variable as parameter, because pyomo's variable isn't the python

问题 my script is: from __future__ import division import numpy import scipy from pyomo.environ import * from pyomo.dae import * from pyomo.opt import SolverFactory m=ConcreteModel() m.x3=Var(within=NonNegativeReals) m.u=Var(within=NonNegativeReals) def _con(m): return m.x3 >=3 m.con=Constraint(rule=_con) def _con2(m): return 4 >= m.u >=1 m.con2=Constraint(rule=_con2) m.obj=Objective(expr=m.x3*m.u) opt = SolverFactory("Ipopt", executable = "/Ipopt-3.12.6/bin/ipopt") results = opt.solve(m) results

问题 Brief background : I am trying to solve an optimization problem where I need to select the best store from where an order can be fulfilled. For this illustration I have 2 orders (O1, O2) and 3 stores (str_1, str_2, str_3). While selecting the best store to fulfill an order, there are 4 factors : A, B, C and D. So for fulfilling order 1, each store will have 4 set of scores corresponding to each factor. Score will be between 0 and 1. I need to determine the optimal weights for 4 factors (wtA,

问题 Brief background : I am trying to solve an optimization problem where I need to select the best store from where an order can be fulfilled. For this illustration I have 2 orders (O1, O2) and 3 stores (str_1, str_2, str_3). While selecting the best store to fulfill an order, there are 4 factors : A, B, C and D. So for fulfilling order 1, each store will have 4 set of scores corresponding to each factor. Score will be between 0 and 1. I need to determine the optimal weights for 4 factors (wtA,

问题 I have a model in pyomo in say machine 1 and I have the CPLEX solver in machine 2. I convert the pyomo model ( ConcreteModel ) into a problem.lp file with function model.write("problem.lp") in machine 1. Then I transfer this file to machine 2 and get the solution.sol file from the CPLEX solver. But names of the variables in the solution file is different from the names of the variables of the model. This is causing a problem in analysis of the solution. Is there any way to convert or map the