pyomo

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')

问题 I am pretty new to optimization in general and pyomo in particular, so I apologize in advance for any rookie mistakes. I have defined a simple unit commitment exercise (example 3.1 from [1]) using [2] as starting point. I got the correct result and my code runs, but I have a few questions regarding how to access stuff. import matplotlib.pyplot as plt import numpy as np import pandas as pd import shutil import sys import os.path import pyomo.environ as pyo import pyomo.gdp as gdp #necessary if

问题 I am trying to formulate some data into a PYOMO model for an optimization problem. materials = ['steel', 'alum', 'carbon', 'cheese'] Each material has 2 properties - density and conductivity and their values are defined as follows. density = { 'steel' : 1.2, 'alum' : 0.8, 'carbon': 1.8, 'cheese': 0.7} conductivity = {'steel' : 6.4, 'alum' : 3.1, 'carbon': 4.4, 'cheese': 0.3} The objective function calculates the weight of 2 rectangular plates as given below: Objective function = Area_1 *

问题 I am trying to formulate some data into a PYOMO model for an optimization problem. materials = ['steel', 'alum', 'carbon', 'cheese'] Each material has 2 properties - density and conductivity and their values are defined as follows. density = { 'steel' : 1.2, 'alum' : 0.8, 'carbon': 1.8, 'cheese': 0.7} conductivity = {'steel' : 6.4, 'alum' : 3.1, 'carbon': 4.4, 'cheese': 0.3} The objective function calculates the weight of 2 rectangular plates as given below: Objective function = Area_1 *

问题 Hei all, I am trying to set up an abstract model for a very simple QP of the form min (x-x0)^2 s.t. A x = b C x <= d I would like to use an abstract model, as I need to resolve with changing parameters (mainly x0, but potentially also A, b, C, d). I am right now struggeling with simply setting the parameters in the model instance. I do not want to use an external data file, but rather internal python variables. All examples I find online use AMPL formatted data files. This is the code I have

问题 Hei all, I am trying to set up an abstract model for a very simple QP of the form min (x-x0)^2 s.t. A x = b C x <= d I would like to use an abstract model, as I need to resolve with changing parameters (mainly x0, but potentially also A, b, C, d). I am right now struggeling with simply setting the parameters in the model instance. I do not want to use an external data file, but rather internal python variables. All examples I find online use AMPL formatted data files. This is the code I have

来源： https://stackoverflow.com/questions/51862749/how-to-formulate-a-piecewise-step-function-in-pyomo

来源： https://stackoverflow.com/questions/62709374/create-a-matrix-based-on-given-constraints

来源： https://stackoverflow.com/questions/62709374/create-a-matrix-based-on-given-constraints

问题 I have been trying to use 'cbc' solver in pyomo as following: """ import pyomo.environ as pe solver = pe.SolverFactory('cbc') result = solver.solve(m) """ Currently, I have been relying on the embedded solver with pyomo installation, which is 'glpk', and I have found that 'cbc' can do better job in many circumstances. I am using: python 3.XX Anaconda environment Windows 10 Pyomo (installed via Conda) It would be great to upgrade the ability of pyomo by using advanced solver! 回答1: You will