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personal:blog:2017:0203_jump_for_gams_users [2017/02/03 16:44]
antonello 		personal:blog:2017:0203_jump_for_gams_users [2023/12/22 11:15]
antonello [Installation]
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 Run, only once, the following code to install JuMP language and a couple of open source solvers: Run, only once, the following code to install JuMP language and a couple of open source solvers:
-<code lang="julia"> +<code julia> 
-Pkg.update()                        # To refresh the list of newest packages +using Pkg               # Load the package manager 
-Pkg.add("JuMP")                     # The mathematical optimisation library +Pkg.update()            # To refresh the list of newest packages 
-Pkg.add("GLPKMathProgInterface")    # A lineaqr and MIP solver +Pkg.add("CSV")          # A library to work with Comma Separated Values 
-Pkg.add("Ipopt")                    # A non-linear solver +Pkg.add("DataFrames")   # A library to deal with dataframes (R like tabular data) 
-Pkg.add("DataFrames")               # A library to deal with dataframes (R-like tabular data)+Pkg.add("JuMP")         # The mathematical optimisation library 
 +Pkg.add("GLPK")         # A linear and MIP solver 
 +Pkg.add("Ipopt")        # A non-linear solver (not needed in this example)
 </code> </code>
  
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 You will need to import as a minima the ''JuMP'' module. If you wish to specify a solver engine rather than letting JuMP select a suitable one, you will need to import also the module relative to the solver, e.g. ''Ipopt'' or  ''GLPKMathProgInterface'' You will need to import as a minima the ''JuMP'' module. If you wish to specify a solver engine rather than letting JuMP select a suitable one, you will need to import also the module relative to the solver, e.g. ''Ipopt'' or  ''GLPKMathProgInterface''
  
-<code  lang="julia"> +<code  julia> 
-# Import of the JuMP and DataFrames modules (the latter one just to import the data from a header-based table, as in the original trasnport example in GAMS +# Import of the JuMP and DataFrames modules (the latter one just to import the data from a header based table, as in the original trasnport example in GAMS 
 using JuMP, DataFrames using JuMP, DataFrames
 </code> </code>
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 <code julia> <code julia>
-## Define sets ##+# Define sets #
 #  Sets #  Sets
 #       i   canning plants   / seattle, san-diego / #       i   canning plants   / seattle, san-diego /
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 <code julia> <code julia>
-## Define parameters ##+# Define parameters #
 #   Parameters #   Parameters
 #       a(i)  capacity of plant i in cases #       a(i)  capacity of plant i in cases
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 <code julia> <code julia>
-# Model declaration +# Model declaration (transport model) 
-trmodel = Model() # transport model+trmodel = Model()  
 </code> </code>
  
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 <code julia> <code julia>
 print(trmodel) print(trmodel)
-</code+</code>
  
 ==== Resolution of the model ==== ==== Resolution of the model ====
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 Here is the complete script:  Here is the complete script: 
  
-<code julia> +<code Julia> 
-#= +# Transport example
-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 implementation: This 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 JuMP, DataFrames+
  
 +# 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 implementation: This 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
 + 
 # Sets # Sets
 plants  = ["seattle","san_diego"]          # canning plants plants  = ["seattle","san_diego"]          # canning plants
 markets = ["new_york","chicago","topeka"]  # markets markets = ["new_york","chicago","topeka"]  # markets
 + 
 # Parameters # Parameters
 a = Dict(              # capacity of plant i in cases a = Dict(              # capacity of plant i in cases
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   "topeka"    => 275,   "topeka"    => 275,
 ) )
 + 
 #  distance in thousands of miles #  distance in thousands of miles
-d_table = wsv"""+d_table = CSV.read(IOBuffer("""
 plants     new_york  chicago  topeka plants     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
-"""+"""), DataFrame, delim=" ", ignorerepeated=true,copycols=true)
 d = Dict( (r[:plants],m) => r[Symbol(m)] for r in eachrow(d_table), m in markets) d = Dict( (r[:plants],m) => r[Symbol(m)] for r in eachrow(d_table), m in markets)
 + 
 f = 90 # freight in dollars per case per thousand miles f = 90 # freight in dollars per case per thousand miles
 + 
 c = Dict() # transport cost in thousands of dollars per case ; c = Dict() # transport cost in thousands of dollars per case ;
 [ c[p,m] = f * d[p,m] / 1000 for p in plants, m in markets] [ c[p,m] = f * d[p,m] / 1000 for p in plants, m in markets]
 + 
 # Model declaration # Model declaration
-trmodel = Model() # transport model +trmodel = Model(GLPK.Optimizer) # transport model 
 + 
 # Variables # Variables
 @variables trmodel begin @variables trmodel begin
     x[p in plants, m in markets] >= 0 # shipment quantities in cases     x[p in plants, m in markets] >= 0 # shipment quantities in cases
 end end
 + 
 # Constraints # Constraints
 @constraints trmodel begin @constraints trmodel begin
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         sum(x[p,m] for p in plants)  >=  b[m]         sum(x[p,m] for p in plants)  >=  b[m]
 end end
 + 
 # Objective # Objective
 @objective trmodel Min begin @objective trmodel Min begin
     sum(c[p,m]*x[p,m] for p in plants, m in markets)     sum(c[p,m]*x[p,m] for p in plants, m in markets)
 end end
 + 
 print(trmodel) print(trmodel)
 + 
 +optimize!(trmodel)
 +status = termination_status(trmodel)
  
-status = solve(trmodel) +if status == MOI.OPTIMAL 
- +    println("Objective value: ", objective_value(trmodel))
-if status == :Optimal +
-    println("Objective value: ", getobjectivevalue(trmodel))+
     println("Shipped quantities: ")     println("Shipped quantities: ")
-    println(getvalue(x))+    println(value.(x))
     println("Shadow prices of supply:")     println("Shadow prices of supply:")
-    [println("$p = $(getdual(supply[p]))") for p in plants]+    [println("$p = $(dual(supply[p]))") for p in plants]
     println("Shadow prices of demand:")     println("Shadow prices of demand:")
-    [println("$m = $(getdual(demand[m]))") for m in markets] +    [println("$m = $(dual(demand[m]))") for m in markets] 
 + 
 else else
     println("Model didn't solved")     println("Model didn't solved")
     println(status)     println(status)
 end end
- +
 # Expected result: # Expected result:
 # obj= 153.675 # obj= 153.675
personal/blog/2017/0203_jump_for_gams_users.txt · Last modified: 2023/12/22 11:39 by antonello
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