Hello, i am currently working on a column generation model which i would like to solve with gurobi in Julia. As i am a total newbie, i dont really know where to start. This is the model i would like to implement.PDF

This is my code for the basic model so far. As i mentioned above, i dont really know how to start and therefore i havent decomposed the code in MP and SP and also didnt try to solve it. I would really appreciate help to setup a working column generation model. Thanks

```
using JuMP, Gurobi
m = Model(solver = GurobiSolver())
# Sets
T = 1:8
S = 1:4
I = 1:4
M = 100
alpha = 0.5
# Demand
demand = Dict(((t,s),val) for (t,s,val) in [(1,1,2), (1,2,1), (1,3,0), (2,1,1), (2,2,2), (2,3,0), (3,1,1), (3,2,1), (3,3,1), (4,1,1), (4,2,2), (4,3,0), (5,1,2), (5,2,0), (5,3,1), (6,1,1), (6,2,1), (6,3,1), (7,1,0), (7,2,3), (7,3,0)])
#Decision variables
@variable(m, slack[t in T, s in S] >= 0, continuous)
@variable(m, motivation[i in I, t in T, s in S] >= 0, ub = 1, continuous)
@variable(m, mood[i in I, t in T] >= 0, ub = 1, continuous)
@variable(m, x[i in I, t in T, s in S] >= 0, bin)
# Objective function
@objective(m, Min, sum(slack[t, s] for t in T, s in S))
# Constraints
for t in T, s in S
@constraint(m, sum(motivation[i, t, s] for i in I) + slack[t, s] == demand[(t,s)])
end
for i in I, s in S, t in T
@constraint(m, mood[i,t] + M * (1 - x[i, t, s]) >= motivation[i, t, s])
@constraint(m, motivation[i, t, s] >= mood[i, t] - M * (1 - x[i, t, s]))
@constraint(m, motivation[i, t, s] <= x[i, t, s])
end
for i in I, t in T
@constraint(m, mood[i, t] == 1 - alpha * sum(x[i, t, s] for s in S))
end
# Solve
optimize!(m)
println("Optimal objective value: ", objective_value(m))
println("slack: ", slack.value)
println("motivation: ", motivation.value)
println("mood: ", mood.value)
println("x: ", x.value)#=
```