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personal:blog:2017:0203_jump_for_gams_users [2017/02/03 14:09]
antonello
personal:blog:2017:0203_jump_for_gams_users [2023/12/22 11:39] (current)
antonello [Further help]
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 You have plenty of development environment to choose from (e.g. Jupiter, Juno), a clear modern language, the possibility to interface your model with third party libraries.. all of this basically for free.\\ You have plenty of development environment to choose from (e.g. Jupiter, Juno), a clear modern language, the possibility to interface your model with third party libraries.. all of this basically for free.\\
 It is also, at least for my user case, much faster than GAMS. Aside the preparation of the model to pass to the solver, where it is roughly equivalent, in the solver execution I can benefit of having on my system a version of IPOPT compiled with the much more performing ma27 linear solver, while for GAMS I would have to rely on the embedded version that is compiled with the MUMPS linear solver. That's part of the flexibility you gain in using JuMP in place of GAMS. It is also, at least for my user case, much faster than GAMS. Aside the preparation of the model to pass to the solver, where it is roughly equivalent, in the solver execution I can benefit of having on my system a version of IPOPT compiled with the much more performing ma27 linear solver, while for GAMS I would have to rely on the embedded version that is compiled with the MUMPS linear solver. That's part of the flexibility you gain in using JuMP in place of GAMS.
-That's said, for people that don't need such flexibility, the package automatically install a local pre-compiled version of the solver, so just adding the package relative to the solver is enough to start writing the model. Even more, for people that doesn't care too much about performances, there is a service on [[https://juliabox.com|JuliaBox.com]] that allows to run Julia/JuMP scripts for free in the browser, without anything to install on the local computer.   +That's said, for people that don't need such flexibility, the package automatically install a local pre-compiled version of the solver, so just adding the package relative to the solver is enough to start writing the model.   
  
  
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 ===== Installation ===== ===== Installation =====
  
-Step 1:  +**Step 1:**  
-Option a: Get an account on [[https://juliabox.com|JuliaBox.com]] to run julia/JuMP script without installing anything on the local computer +  Option a: Get an account on [[https://juliabox.com|JuliaBox.com]] to run julia/JuMP script without installing anything on the local computer 
-Option b: Install Julia for your platform ([[http://julialang.org/downloads/|http://julialang.org/downloads/]]+  Option b: Install Julia for your platform ([[http://julialang.org/downloads/|http://julialang.org/downloads/]])
-Step 2: +
-Run, only once, the following code to install JuMP language and a couple of open source solvers: +
-Pkg.update()                        # To refresh the list of newest packages +
-Pkg.add("JuMP"                    # The mathematical optimisation library +
-Pkg.add("GLPKMathProgInterface"   # A lineaqr and MIP solver +
-Pkg.add("Ipopt"                   # A non-linear solver +
-Pkg.add("DataFrames"              # A library to deal with dataframes (R-like tabular data)+
  
-==== Ubuntu ==== +**Step 2:**
-//(tested in Ubuntu 14.04 LTS)//+
  
-  * **Install the python pre-requisites:** +Run, only once, the following code to install JuMP language and a couple of open source solvers
-    * ''sudo apt-get install python-yaml, python-pip''  +<code julia> 
-  * **Install pyomo:** +using Pkg               # Load the package manager 
-    * ''sudo pip install pyomo'' +Pkg.update()            # To refresh the list of newest packages 
-    * ''sudo pip install pyomo.extras'' +Pkg.add("CSV"         # A library to work with Comma Separated Values 
-  * **Install solvers:** +Pkg.add("DataFrames"  # A library to deal with dataframes (R like tabular data) 
-    * //linear and MIP solver (glpk)//: ''sudo apt-get install glpk36 glpk-utils'' +Pkg.add("JuMP"        # The mathematical optimisation library 
-    * //non-linaer solver (ipopt)//: ''sudo apt-get install coinor-libipopt1'' +Pkg.add("GLPK"        # A linear and MIP solver 
- +Pkg.add("Ipopt"       # A non-linear solver (not needed in this example
-==== Windows and Mac ==== +</code>
-Please refer to the [[https://software.sandia.gov/downloads/pub/coopr/CooprInstallGuide.html|Coopr installation guide]]+
  
 ===== Model components ===== ===== Model components =====
  
-==== Creation of the Model ==== +==== Importing the libraries ==== 
-In pyomo everything is an object. The various components of the model (sets, parameters, variables, constrains, objective..) are all attributes of the main model object while being objects themselves.\\ + 
-There are two type of models in pyomo: A ''ConcreteModel'' is one where all the data is defined at the model creation. We are going to use this type of model in this tutorial. Pyomo however supports also an ''AbstractModel'', where the model structure is firstly generated and then particular instances of the model are generated with a particular set of data.\\ +You will need to import as a minima the ''JuMP'' module and a suitable solver. In this case the problem is linear, so we can use ''GLPK'' (''HiGHS'' is another popular alternative)If the problem would have been non-linear, you could have used the ''Ipopt'' solver/package 
-The first thing to do in the script is hence to load the pyomo library and to create a new ConcreteModel (we have little imagination hereand we call our model "model". You can give it whatever name you want((However, if you give your model an other name, you also need to add a ''pyomo_create_model(options=None, model_options=None)'' function that returns your model))): + 
-<code python+<code  julia
-# Import of the pyomo module +# Import of the JuMP, GLPK, CSV and DataFrames modules (the latter twos just to import the data from a header based table, as in the original trasnport example in GAMS  
-from pyomo.environ import * +using CSV, DataFrames, GLPK, JuMP
-                 +
-# Creation of Concrete Model +
-model = ConcreteModel()+
 </code> </code>
  
-==== Set definition ==== +==== Defining the "sets" ==== 
-Sets are created as attributes object of the main model objects and all the information is given as parameter in the constructor functionSpecificallywe are passing to the constructor the initial elements of the set and a documentation string to keep track on what our set represents+ 
-<code python+JuMP doesn't really have a concept of sets, but it uses the native containers available in the core Julia language\\Variables, parameters and constraints can be indexed using these containers.\\ 
-## Define sets ##+While many works with position-based listsI find more readable using dictionaries instead. So the "sets" are represented as lists, but then everything else is a dictionary with the elements of the list as keys.\\ 
 +One noteit seems that Julia/JuMP don't like much the "-" symbol, so I replaced it to "_".\\ 
 +  
 +<code julia
 +# Define sets #
 #  Sets #  Sets
 #         canning plants   / seattle, san-diego / #         canning plants   / seattle, san-diego /
 #         markets          / new-york, chicago, topeka / ; #         markets          / new-york, chicago, topeka / ;
-model.i = Set(initialize=['seattle','san-diego'], doc='Canning plans') +plants  = ["seattle","san_diego"         # canning plants 
-model.j = Set(initialize=['new-york','chicago''topeka'], doc='Markets')+markets = ["new_york","chicago","topeka" # markets
 </code> </code>
  
-==== Parameters ==== + 
-Parameter objects are created specifying the sets over which they are defined and are initialised with either python dictionary or a scalar: +==== Definition of the "parameters" ==== 
-<code python+ 
-## Define parameters ##+Capacity of plants and demand of markets are directly defined as dictionaries, while the distance is first read as a DataFrame from a white-space separated table and then it is converted in "(plant, market) => value" dictionary
 + 
 +<code julia
 +# Define parameters #
 #   Parameters #   Parameters
 #       a(i)  capacity of plant i in cases #       a(i)  capacity of plant i in cases
 #         /    seattle     350 #         /    seattle     350
 #              san-diego   600  / #              san-diego   600  /
 +a = Dict(              # capacity of plant i in cases
 +  "seattle"   => 350,
 +  "san_diego" => 600,
 +)
 +
 #       b(j)  demand at market j in cases #       b(j)  demand at market j in cases
 #         /    new-york    325 #         /    new-york    325
 #              chicago     300 #              chicago     300
 #              topeka      275  / ; #              topeka      275  / ;
-model.a Param(model.i, initialize={'seattle':350,'san-diego':600}, doc='Capacity of plant i in cases') +Dict             # demand at market j in cases 
-model.b Param(model.j, initialize={'new-york':325,'chicago':300,'topeka':275}doc='Demand at market j in cases'+  "new_york"  =325, 
- Table d(i,j)  distance in thousands of miles+  "chicago  => 300, 
 +  "topeka   => 275, 
 +) 
 + 
 +# Table d(i,j)  distance in thousands of miles
 #                    new-york       chicago      topeka #                    new-york       chicago      topeka
 #      seattle          2.5           1.7          1.8 #      seattle          2.5           1.7          1.8
 #      san-diego        2.5           1.8          1.4  ; #      san-diego        2.5           1.8          1.4  ;
-dtab { +d_table CSV.read(IOBuffer(""" 
-    ('seattle',  'new-york') : 2.5+plants     new_york  chicago  topeka 
-    ('seattle',  'chicago'1.7+seattle    2.5       1.7      1.8 
-    ('seattle',  'topeka'  : 1.8, +san_diego  2.5       1.8      1.4 
-    ('san-diego','new-york') : 2.5+""")DataFrame, delim=" ", ignorerepeated=true,copycols=true) 
-    ('san-diego','chicago'1.8+d = Dict(r[:plants],m) => r[Symbol(m)] for r in eachrow(d_table)in markets
-    ('san-diego','topeka'  : 1.4, +Here we are converting the table in a "(plant, market) => distance" dictionary 
-    } +# r[:plants]:   the first key, row field using a fixed header 
-model.d = Param(model.imodel.j, initialize=dtabdoc='Distance in thousands of miles'+# m:            the second key 
- Scalar f  freight in dollars per case per thousand miles  /90/ ; +# r[Symbol(m)]: the value, the row field with a dynamic header 
-model.= Param(initialize=90, doc='Freight in dollars per case per thousand miles') + 
-</code> +Scalar f  freight in dollars per case per thousand miles  /90/ ; 
-A third, powerful way to initialize a parameter is using a user-defined function.\\ +f = 90 # freight in dollars per case per thousand miles  
-This function will be automatically called by pyomo with any possible (i,j) set. In this case pyomo will actually call c_init() six times in order to initialize the model.c parameter. + 
-<code python> +# Parameter c(i,j)  transport cost in thousands of dollars per case ;
- Parameter c(i,j)  transport cost in thousands of dollars per case ;+
 #            c(i,j) = f * d(i,j) / 1000 ; #            c(i,j) = f * d(i,j) / 1000 ;
-def c_init(model, i, j): +# We first declare an empty dictionary and then we fill it with the values 
-  return model.f * model.d[i,j] / 1000 +c = Dict() # transport cost in thousands of dollars per case ; 
-model.c = Param(model.i, model.jinitialize=c_init, doc='Transport cost in thousands of dollar per case')+[ c[p,m] = f * d[p,m] / 1000 for p in plantsin markets] 
 </code> </code>
 +The above code take advantage of [[http://docs.julialang.org/en/stable/manual/arrays/#comprehensions|List Comprehensions]], a powerful feature of the Julia language that provides a concise way to loop over a list.
 +If we take the creation of the d dictionary as example, without List Comprehensions we would have had to write a nested for loop like:
 +<code julia>
 +d = Dict()
 +for r in eachrow(d_table)
 +  for m in markets
 +    d = (r[:plants],m) => r[Symbol(m)]
 +  end
 +end
 +</code>
 +Using List Comprehension is however quicker to code and more readable.
 +
 +
 +==== Declaration of the model ====
 +
 +Here we declare a JuML optimisation model and we give it a name. This name will be then passed as first argument to all the subsequent operations, like creation of variables, constraints and objective function.\\
 +The solver engine to use is given as argument of the ''Model()'' call.\\
 +We could pass solver-specific options with the ''set_optimizer_attribute'' function, e.g.:
 +''set_optimizer_attribute(trmodel, "msg_lev", GLPK.GLP_MSG_ON)''
  
-==== Variables ==== +<code julia> 
-Similar to parametersvariables are created specifying their domain(s). For variables we can also specify the upper/lower bounds in the constructor.\\+# Model declaration (transport model) 
 +trmodel Model(GLPK.Optimizer)  
 +</code> 
 + 
 +==== Declaration of the model variables ==== 
 + 
 +Variables can have multiple-dimensions - that isbeing indexed under several indexes -, and bounds are given at the same time as their declaration.\\
 Differently from GAMS, we don't need to define the variable that is on the left hand side of the objective function. Differently from GAMS, we don't need to define the variable that is on the left hand side of the objective function.
-<code python>+ 
 +<code julia>
 ## Define variables ## ## Define variables ##
 #  Variables #  Variables
Line 123: Line 152:
 #             total transportation costs in thousands of dollars ; #             total transportation costs in thousands of dollars ;
 #  Positive Variable x ; #  Positive Variable x ;
-model.= Var(model.imodel.j, bounds=(0.0,None), doc='Shipment quantities in case')+@variables trmodel begin 
 +    x[p in plantsm in markets] >= 0 # shipment quantities in cases 
 +end
 </code> </code>
  
-==== Constrains ==== +==== Declaration of the model constraints ==== 
-At this pointit should not be a surprise that constrains are again defined as model objects with the required information passed as parameter in the constructor function.   + 
-<code python>+As in GAMSeach constraint can actually be a "family" of constraints: 
 + 
 +<code julia>
 ## Define contrains ## ## Define contrains ##
 # supply(i)   observe supply limit at plant i # supply(i)   observe supply limit at plant i
 # supply(i) .. sum (j, x(i,j)) =l= a(i) # supply(i) .. sum (j, x(i,j)) =l= a(i)
-def supply_rule(model, i): 
-  return sum(model.x[i,j] for j in model.j) <= model.a[i] 
-model.supply = Constraint(model.i, rule=supply_rule, doc='Observe supply limit at plant i') 
 # demand(j)   satisfy demand at market j ;   # demand(j)   satisfy demand at market j ;  
 # demand(j) .. sum(i, x(i,j)) =g= b(j); # demand(j) .. sum(i, x(i,j)) =g= b(j);
-def demand_rule(modelj): +@constraints trmodel begin 
-  return sum(model.x[i,j] for in model.i>model.b[j  +    supply[p in plants]  # observe supply limit at plant p 
-model.demand = Constraint(model.jrule=demand_rule, doc='Satisfy demand at market j')+        sum(x[p,m] for in markets < a[p
 +    demand[m in markets] # satisfy demand at market 
 +        sum(x[p,m] for p in plants > b[m] 
 +end
 </code> </code>
-The above code take advantage of [[https://docs.python.org/2/tutorial/datastructures.html#list-comprehensions|List Comprehensions]]a powerful feature of the python language that provides concise way to loop over a list. + 
-If we take the supply_rule as example, this is actually called two times by pyomo (once for each of the elements of i)Without List Comprehensions we would have had to write our function using a for loop, like: +==== Declaration of the model objective ==== 
-<code python+ 
-def supply_rule(model, i): +Contrary to constraints and variables, the objective is always unique functionNote that it is at this point that we specify the direction of the optimisation 
-  supply = 0.0 + 
-  for j in model.j: +<code julia
-    supply += model.x[i,j+# Objective 
-  return supply <= model.a[i]+@objective trmodel Min begin 
 +    sum(c[p,m]*x[p,mfor p in plants, m in markets) 
 +end
 </code> </code>
-Using List Comprehension is however quicker to code and more readable. 
  
-==== Objective & solving ==== +==== Human-readable visualisation of the model (optional) ==== 
-The definition of the objective is similar to those of the constrains, except that most solvers require a scalar objective function, hence a unique function, and we can specify the sense (direction) of the optimisation. + 
-<code python> +If we wish we can get the optimisation model printed in a human-readable fashionso we can expect all is like it should be 
-## Define Objective and solve ## + 
-#  cost        define objective function +<code julia
-#  cost ..        z  =e=  sum((i,j), c(i,j)*x(i,j)) ; +print(trmodel)
-#  Model transport /all/ ; +
-#  Solve transport using lp minimizing z ; +
-def objective_rule(model): +
-  return sum(model.c[i,j]*model.x[i,j] for i in model.i for j in model.j) +
-model.objective Objective(rule=objective_rule, sense=minimize, doc='Define objective function') +
-</code> +
-As we are here looping over two distinct sets, we can see how List Comprehension really simplifies the code. The objective function could have being written without List Comprehension as: +
-<code python+
-def objective_rule(model)+
-  obj = 0.0   +
-  for ki in model.i: +
-    for kj in model.j: +
-      obj += model.c[ki,kj]*model.x[ki,kj] +
-  return obj+
 </code> </code>
  
-==== Retrieving the output ==== +==== Resolution of the model ==== 
-To retrieve the output and do something with it (either to just display it -like we do here-, to plot a graph with [[http://matplotlib.org|matplotlib]] or to save it in a csv file) we use the ''pyomo_postprocess()'' function.\\ + 
-This function is called by pyomo after the solver has finished.  +It is at this point that the solver is called and the model is passed to the solver engine for its solutionThe return value is the status of the optimisation (''MOI.OPTIMAL'' if all went fine) 
-<code python+ 
-## Display of the output ## +<code julia
-# Display x.l, x.m ; +optimize!(trmodel
-def pyomo_postprocess(options=None, instance=None, results=None): +status = termination_status(trmodel)
-  model.x.display()+
 </code> </code>
-We can print model structure information with ''model.pprint()'' ("pprint" stand for "pretty print").\\ 
-Results are also by default saved in a results.json file or, if PyYAML is installed in the system, in results.yml. 
  
-==== Editing and running the script ==== +==== Visualisation of the results ==== 
-Differently from GAMS you can use whatever editor environment you wish to code a pyomo script. If you don't need debugging features, a simple text editor like Notepad++ (in windows), gedit or kate (in Linuxwill suffice. They already have syntax highlight for python.\\ +While you can do any fancy output you may wish after you retrieve the optimal value of the variables with ''getvalue(var_name)''you can just ''println(getvalue(x))'' to get a basic output.\\ 
-If you want advanced features and debugging capabilities you can use a dedicated Python IDE, like e.g. [[https://code.google.com/p/spyderlib/|Spyder]].+Notice that you can also easily retrieve the dual value associated to the constraint with ''getdual(constraint_name)''.
  
-You will normally run the script as ''pyomo solve --solver=glpk transport.py''. You can output solver specific output adding the option ''--stream-output''.\\ +<code julia
-If you want to run the script as ''python transport.py'' add the following lines at the end:\\ +if status == MOI.OPTIMAL 
-<code python> +    println("Objective value: ", objective_value(trmodel)) 
-# This is an optional code path that allows the script to be run outside of +    println("Shipped quantities: ") 
-# pyomo command-line.  For example:  python transport.py +    println(value.(x)) 
-if __name__ == '__main__': +    println("Shadow prices of supply:") 
-    #This replicates what the pyomo command-line tools does +    [println("$p $(dual(supply[p]))") for p in plants] 
-    from pyomo.opt import SolverFactory +    println("Shadow prices of demand:"
-    import pyomo.environ +    [println("$m = $(dual(demand[m]))") for m in markets] 
-    opt = SolverFactory("glpk") +  
-    instance model.create() +else 
-    results = opt.solve(instance+    println("Model didn't solved"
-    #sends results to stdout +    println(status) 
-    results.write() +end
-    pyomo_postprocess(None, instance, results)+
 </code> </code>
  
-Finally, if you are very lazy and want to run the script with just ./transport.py (and you are in Linux) add the following lines at the top: + 
-<code python> +==== Editing and running the script ==== 
-#!/usr/bin/env python +Differently from GAMS you can use whatever editor environment you wish to code a JuMP scriptIf you don't need debugging features, a simple text editor like Notepad++ (in windows), gedit or kate (in Linux) will suffice. They already have syntax highlight for Julia.\\ 
-# -*- coding: utf-8 -*- +If you want advanced features and debugging capabilities you can use a dedicated Julia IDE, like the [[https://www.julia-vscode.org/|Julia extension for VSCode]]. 
-</code>+ 
 +If you are using instead the Julia terminal,  you can run the script as ''julia transport.jl''.
  
 ===== Further help ===== ===== Further help =====
-Documentation of pyomo is available from [[https://software.sandia.gov/trac/coopr/wiki/Documentation|this page]]. However if you want to do serious things with pyomoit is most likely that you will have to either look at the source code or consult the [[https://groups.google.com/forum/#!forum/coopr-forum|mailing list]].+Documentation of JuMP is available from [[https://jump.dev/|this page]], and community-based support is available on [[https://discourse.julialang.org/c/domain/opt|the Discourse forum]]. 
 + 
 +Happy modelling with JuMP ;-)
  
-Happy modelling with pyomo ;-) 
 ===== Complete script ===== ===== Complete script =====
  
 Here is the complete script:  Here is the complete script: 
  
-<code python+<code Julia
-#!/usr/bin/env python +Transport example 
-# -*codingutf-8 -*-+ 
 +# Transposition in JuMP of the basic transport model used in the GAMS tutorial 
 +#  
 +# This problem finds a least cost shipping schedule that meets 
 +# requirements at markets and supplies at factories. 
 + 
 +# - Original formulation: Dantzig, G B, Chapter 3.3. In Linear Programming and Extensions. 
 +# Princeton University Press, Princeton, New Jersey, 1963. 
 +Gams implementationThis formulation is described in detail in: 
 +# Rosenthal, R E, Chapter 2: A GAMS Tutorial. In GAMS: A User's Guide. 
 +# The Scientific Press, Redwood City, California, 1988. 
 +JuMP implementation: Antonello Lobianco
    
-""" +using CSV, DataFrames, GLPK, JuMP
-Basic example of transport model from GAMS model library translated to Pyomo+
    
-To run this you need pyomo and a linear solver installed. +# Sets 
-When these dependencies are installed you can solve this example in one of +plants  = ["seattle","san_diego"         # canning plants 
-this ways (glpk is the default solver):+markets = ["new_york","chicago","topeka" # markets
    
-    ./transport.py (Linux only+# Parameters 
-    python transport.py +a = Dict             # capacity of plant i in cases 
-    pyomo solve transport.py +  "seattle"   => 350, 
-    pyomo solve --solver=glpk transport.py+  "san_diego" => 600, 
 +
 +b = Dict(              # demand at market j in cases 
 +  "new_york"  => 325, 
 +  "chicago"   => 300, 
 +  "topeka"    => 275, 
 +)
    
-To display the results:+#  distance in thousands of miles 
 +d_table = CSV.read(IOBuffer(""" 
 +plants     new_york  chicago  topeka 
 +seattle    2.5       1.7      1.8 
 +san_diego  2.5       1.8      1.4 
 +"""), DataFrame, delim=" ", ignorerepeated=true,copycols=true) 
 +d = Dict( (r[:plants],m) => r[Symbol(m)] for r in eachrow(d_table), m in markets)
    
-    cat results.json +f = 90 # freight in dollars per case per thousand miles
-    cat results.yml (if PyYAML is installed on your system)+
    
-GAMS equivalent code is inserted as single-dash comments. The original GAMS code +c = Dict() # transport cost in thousands of dollars per case ; 
-needs slighly different ordering of the commands and it's available at +[ c[p,m] = f * d[p,m] 1000 for p in plants, m in markets]
-http://www.gams.com/mccarl/trnsport.gms+
    
-Original problem formulation: +# Model declaration 
-    Dantzig, G B, Chapter 3.3. In Linear Programming and Extensions. +trmodel = Model(GLPK.Optimizer) # transport model
-    Princeton University Press, Princeton, New Jersey, 1963. +
-GAMS implementation: +
-    Rosenthal, R E, Chapter 2: A GAMS Tutorial. In GAMS: A User's Guide. +
-    The Scientific Press, Redwood City, California, 1988. +
-Pyomo translation: +
-    Antonello Lobianco+
    
-This file is in the Public Domain +# Variables 
-"""+@variables trmodel begin 
 +    x[p in plants, m in markets] >= 0 # shipment quantities in cases 
 +end
    
-Import +Constraints 
-from pyomo.environ import *+@constraints trmodel begin 
 +    supply[p in plants],   # observe supply limit at plant p 
 +        sum(x[p,m] for m in markets)  <=  a[p] 
 +    demand[m in markets],  # satisfy demand at market m 
 +        sum(x[p,m] for p in plants)  >=  b[m] 
 +end
    
-Creation of a Concrete Model +Objective 
-model = ConcreteModel()+@objective trmodel Min begin 
 +    sum(c[p,m]*x[p,m] for p in plants, m in markets) 
 +end
    
-## Define sets ## +print(trmodel)
-#  Sets +
-#         canning plants   / seattle, san-diego / +
-#         markets          / new-york, chicago, topeka / ; +
-model.i = Set(initialize=['seattle','san-diego'], doc='Canning plans'+
-model.j = Set(initialize=['new-york','chicago', 'topeka'], doc='Markets')+
    
-## Define parameters ## +optimize!(trmodel
-#   Parameters +status = termination_status(trmodel
-#       a(i capacity of plant i in cases + 
-#         /    seattle     350 +if status =MOI.OPTIMAL 
-#              san-diego   600 +    println("Objective value"objective_value(trmodel)) 
-#       b(j demand at market j in cases +    println("Shipped quantities: "
-#         /    new-york    325 +    println(value.(x)
-#              chicago     300 +    println("Shadow prices of supply:"
-#              topeka      275  / ; +    [println("$p = $(dual(supply[p]))") for p in plants] 
-model.a Param(model.i, initialize={'seattle':350,'san-diego':600}, doc='Capacity of plant i in cases') +    println("Shadow prices of demand:"
-model.b = Param(model.j, initialize={'new-york':325,'chicago':300,'topeka':275}, doc='Demand at market j in cases') +    [println("$m $(dual(demand[m]))"for m in markets]
-#  Table d(i,j distance in thousands of miles +
-#                    new-york       chicago      topeka +
-#      seattle          2.5           1.7          1.8 +
-#      san-diego        2.5           1.8          1.4  ; +
-dtab = { +
-    ('seattle',  'new-york': 2.5, +
-    ('seattle',  'chicago' : 1.7, +
-    ('seattle',  'topeka'  : 1.8, +
-    ('san-diego','new-york'2.5, +
-    ('san-diego','chicago' : 1.8, +
-    ('san-diego','topeka'  : 1.4, +
-    +
-model.d = Param(model.i, model.j, initialize=dtab, doc='Distance in thousands of miles'+
-#  Scalar f  freight in dollars per case per thousand miles  /90/ ; +
-model.f = Param(initialize=90, doc='Freight in dollars per case per thousand miles'+
-#  Parameter c(i,j)  transport cost in thousands of dollars per case ; +
-#            c(i,j= f * d(i,j/ 1000 ; +
-def c_init(model, i, j)+
-  return model.f * model.d[i,j/ 1000 +
-model.c = Param(model.i, model.j, initialize=c_init, doc='Transport cost in thousands of dollar per case')+
    
-## Define variables ## +else 
-#  Variables +    println("Model didn't solved"
-#       x(i,j)  shipment quantities in cases +    println(status
-#             total transportation costs in thousands of dollars ; +end
-#  Positive Variable x ; +
-model.x = Var(model.i, model.j, bounds=(0.0,None), doc='Shipment quantities in case') +
-  +
-## Define contrains ## +
-# supply(i  observe supply limit at plant i +
-# supply(i) .. sum (j, x(i,j)) =l= a(i) +
-def supply_rule(model, i): +
-  return sum(model.x[i,j] for j in model.j) <= model.a[i] +
-model.supply = Constraint(model.i, rule=supply_rule, doc='Observe supply limit at plant i') +
-# demand(j)   satisfy demand at market j ;   +
-# demand(j) .. sum(i, x(i,j)) =g= b(j); +
-def demand_rule(model, j): +
-  return sum(model.x[i,j] for i in model.i) >= model.b[j]   +
-model.demand = Constraint(model.j, rule=demand_rule, doc='Satisfy demand at market j')+
  
-## Define Objective and solve ## 
-#  cost        define objective function 
-#  cost ..        z  =e=  sum((i,j), c(i,j)*x(i,j)) ; 
-#  Model transport /all/ ; 
-#  Solve transport using lp minimizing z ; 
-def objective_rule(model): 
-  return sum(model.c[i,j]*model.x[i,j] for i in model.i for j in model.j) 
-model.objective = Objective(rule=objective_rule, sense=minimize, doc='Define objective function') 
- 
-  
-## Display of the output ## 
-# Display x.l, x.m ; 
-def pyomo_postprocess(options=None, instance=None, results=None): 
-  model.x.display() 
-  
-# This is an optional code path that allows the script to be run outside of 
-# pyomo command-line.  For example:  python transport.py 
-if __name__ == '__main__': 
-  
-    #This replicates what the pyomo command-line tools does 
-    from pyomo.opt import SolverFactory 
-    import pyomo.environ 
-    opt = SolverFactory("glpk") 
-    instance = model.create() 
-    results = opt.solve(instance) 
-    #sends results to stdout 
-    results.write() 
-    pyomo_postprocess(None, instance, results) 
-  
 # Expected result: # Expected result:
 # obj= 153.675 # obj= 153.675
Line 360: Line 333:
 #['san-diego','topeka'  = 275 #['san-diego','topeka'  = 275
 </code> </code>
- 
- 
- 
-~~DISCUSSION~~ 
- 
- 
- 
  
 ~~DISCUSSION~~ ~~DISCUSSION~~
  
personal/blog/2017/0203_jump_for_gams_users.1486127366.txt.gz · Last modified: 2018/06/18 15:10 (external edit)
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