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--- personal:blog:2017:0203_jump_for_gams_users [2023/12/22 11:19]
antonello [Importing the libraries]
+++ personal:blog:2017:0203_jump_for_gams_users [2023/12/22 11:24]
antonello [Declaration of the model]
@@ Line 96: / Line 96: @@
 #      seattle          2.5           1.7          1.8
 #      san-diego        2.5           1.8          1.4  ;
-d_table = wsv"""
+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)
 # Here we are converting the table in a "(plant, market) => distance" dictionary
@@ Line 132: / Line 132: @@
 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.\\
-We can, if we wish, works with several models at the same time.\\
+The solver engine to use is given as argument of the ''Model()'' call.\\
-If we do not specify a solver, we let JuML use a suitable solver for the type of problem. Aside to specify the solver, we can also pass it solver-level options, e.g.:
+We could pass solver-specific options with the ''set_optimizer_attribute'' function, e.g.:
-''mymodel = Model(solver=IpoptSolver(print_level=0))''
+''set_optimizer_attribute(trmodel, "msg_lev", GLPK.GLP_MSG_ON)''
 <code julia>
 # Model declaration (transport model)
-trmodel = Model()
+trmodel = Model(GLPK.Optimizer)
 </code>