Computing Irreducible Inconsistent Subsystem (IIS) using Julia JuMP (Gurobi) - julia

Question

Trying to compute IIS for my stupidly overcomplicated model.
I'll include the whole model for clarity:
using JuMP
using Gurobi
import XLSX
roster = Model(Gurobi.Optimizer)
Intern = 1:11 #i
Week = 1:52 #k
Rotation = 1:23 #j
Leave_week = 1:3
Dec_leave = 1:2
M = 1000
clins = 7:52
non_clins = 5:52
early = 5:28
gen = [5,8]
#variables(roster, begin
x[Intern,Week, Rotation], Bin
y[Intern,Week, Rotation], Bin
L[Leave_week, Week], Bin
D[Dec_leave, Intern], Bin
s[Intern, Week], Bin
g[Intern, gen], Bin
end
)
#physical constraint
#constraint(roster, phys[i in Intern, k in Week],
sum(x[i,k,j] for j in Rotation) == 1)
#Rotation capacity
rots = [1,2,3,4,5,6,7,8,9,10,12,13,15,16,17,18,19]
cap_rhs = [2,1,1,1,1,1,1,1,1, 1, 1, 1, 2, 1, 1, 2, 1, 1]
cap = #constraint(roster, [(b,d) in zip(rots, cap_rhs), k in 1:52],
sum(x[i,k,b] for i in Intern) <= d)
#dispensary
disp = #constraint(roster, [i in Intern], sum(x[i,k,j] for k in Week, j in 14:18) >= 5)
disp1 = #constraint(roster, [i in Intern], sum(x[i,k,j] for k in 29:40, j in 14:18) >= 1)
disp2 = #constraint(roster, [i in Intern], sum(x[i,k,j] for k in 41:52, j in 14:18) >= 1)
clay_cap_o = #constraint(roster, [k in 1:4],
sum(x[i,k,14] for i in Intern) <=3)
clay_cap_o = #constraint(roster, [k in 5:52],
sum(x[i,k,14] for i in Intern) <=2)
#Orientation
IP_1 = #constraint(roster, [i in Intern], sum(x[i,k,1] for k in 1:6) >= 1)
IP_3_1 = #constraint(roster, [i in Intern], sum(s[i,k] for k in 1:5) <= 1)
IP_3_11 = #constraint(roster, sum(s[i,k] for i in Intern, k in 1:5) == 10)
IP_3_2 = #constraint(roster, [i in Intern, k in 1:4], x[i,k,1] == s[i,k])
# IP_lazy = #constraint(roster, [(i,k) in zip(Intern, [1 1 2 2 3 3 4 4 5 5 6])], x[i,k,1] ==1)
orien = #constraint(roster, [i in Intern], sum(x[i,k,j] for k in 1:4, j in [1,14,15,16,17,18] ) == 4)
orien1 = #constraint(roster, [i in Intern, j in [1,14,15,16,17,18]], sum(x[i,k,j] for k in 1:4 ) <= 2)
#leave
# 2 weeks leave
#constraint(roster, [i in Intern],
sum(x[i,k,j] for k in Week, j in 20:22) == 2)
week1_dvar = #constraint(roster, sum(L[1, k] for k in 17:22) == 1)
#constraint(roster, week1[k in 17:22], sum(x[i,k,20] for i in Intern) == 11*L[1,k])
#constraint(roster, week2_3_dvar[l in 2:3], sum(L[l, k] for k in 35:41) == 1)
#constraint(roster, week2_3[(l, j, rhs) in zip(2:3, 21:22, [6,5]), k in 35:41],
sum(x[i,k,j] for i in Intern) == rhs*L[l, k] )
#constraint(roster, max_leave[i in Intern], sum(x[i,k,j] for j in 20:22, k in Week) ==2)
## - Dec_leave
#constraint(roster,[i in Intern], sum(D[l,i] for l in 1:2) == 1)
#constraint(roster, [(l,d) in zip(1:2,[6,5])], sum(D[l,i] for i in Intern) == d)
#constraint(roster, [i in Intern, (l,b) in zip(1:2, [49:50, 51:52])],
sum(x[i,k,23] for k in b) == 2*D[l,i])
#constraint(roster, [i in Intern], sum(x[i,k,23] for k in Week) == 2)
#MIC
MIC_1_dvar = #constraint(roster, [i in Intern], sum(y[i,k,4] for k in 5:27 ) == 1)
MIC_2_dvar = #constraint(roster, [i in Intern], sum(y[i,k,4] for k in 29:51 ) == 1)
MIC = #constraint(roster, [ i in Intern, k in 5:27],
2 - sum(x[i, k + alpha, 4] for alpha in 0:1 ) <= M*(1-y[i,k,4]))
MIC = #constraint(roster, [ i in Intern, k in 29:51],
2 - sum(x[i, k + alpha, 4] for alpha in 0:1 ) <= M*(1-y[i,k,4]))
#gen_med
g_vars = #constraint(roster, [i in Intern], sum(g[i,m] for m in gen) ==1)
gen_duration_dvar = #constraint(roster, [(b,d) in zip(gen,[6,7]), i in Intern],
sum(y[i,k,b] for k in 1:(52 - (d-1) ) ) == g[i,b])
gen_limit = #constraint(roster, [(b,d) in zip(gen,[6,7]), i in Intern],
sum(x[i,k,b] for k in Week) == g[i,b]*d)
gen_durations = #constraint(roster, [(b,d) in zip(gen,[6,7]),
i in Intern, k in 1:(52 - (d-1) )],
d - sum(x[i, k + alpha, b] for alpha in 0:(d-1) ) <= M*(1-y[i,k,b]))
ed_with_gen = #constraint(roster, [i in Intern, k in 2:50], y[i,k,23] - x[i,k-1,8] - x[i,k+2,8] <= (1-g[i,5]))
#qum
qum_1 = #constraint(roster, [i in Intern], sum(x[i,k,13] for k in early) >= 1)
qum_2 = #constraint(roster, [i in Intern], sum(x[i,k,13] for k in 1:39) == 2)
# duration
dur_rot = [2,6,7,9,10,11,19]
durs = [2,4,2,3, 3, 4, 2]
duration_dvar = #constraint(roster, [(b,d) in zip(dur_rot, durs), i in Intern],
sum(y[i,k,b] for k in 1:(52 - (d-1) ) ) == 1)
durations = #constraint(roster, [(b,d) in zip(dur_rot, durs),
i in Intern, k in 1:(52 - (d-1) )],
d - sum(x[i, k + alpha, b] for alpha in 0:(d-1) ) <= M*(1-y[i,k,b]))
AP_dur_var = #constraint(roster, [i in Intern], sum(y[i,k,3] for k in 5:35) == 1)
AP_dur = #constraint(roster, [i in Intern, k in 5:35],
2 - sum(x[i,k + alpha, 3] for alpha in 0:1) <= M*(1 - y[i,k,3]))
AP_third = #constraint(roster,[i in Intern], sum(x[i,k,3] for k in 37:52) == 1)
# rotations_lengths
completion = #constraint(roster,
[(j,c,d) in zip([1,2,3,4,6,7,9,10,11,12,19],
[ 1:28,clins,non_clins, non_clins,clins,clins, clins, clins, 29:52, early, clins],
[3,2,3,4,4,2,3, 3, 4, 1, 2]), i in Intern],
sum(x[i,k,j] for k in c) == d)
IP_soft = #constraint(roster, [i in Intern], sum(x[i,k,1] for k in Week) >= 5)
whole_year = #constraint(roster, [(j,d) in zip([1,2,3,4,6,7,9,10,11,12,13,19],
[5,2,3,4,4,2,3, 3, 4, 1, 2, 2]), i in Intern],
sum(x[i,k,j] for k in Week) == d)
# public holiday constraints
no_pubs = #constraint(roster,
[i in Intern, k in [4,7,10,13,14,22,24,29,39,44,52], j in [12,13]],
x[i,k,j] == 0 )
z = #expression(roster, sum(x[i,k,j] for i in Intern, j in Rotation, k in Week))
obj_z = #objective(roster, Max, z)
optimize!(roster)
Tried a few approaches, such as those recommended here and here but ran into errors.
Tried Gurobi.computeIIS(roster) but came up with:
The C API of Gurobi.jl has been rewritten to expose the complete C API, and
all old functions have been removed. For more information, see the Discourse
announcement: https://discourse.julialang.org/t/ann-upcoming-breaking-changes-to-cplex-jl-and-gurobi-jl
Here is a brief summary of the changes.
...
Any help or advise appreciated. Please feel free to edit the example for clarity.
Many thanks.

Answer 1

This is a little advanced, and some of the plumbing is missing (you have to use direct_model at present), but you can go:
using JuMP, Gurobi
model = direct_model(Gurobi.Optimizer())
#variable(model, x >= 0)
#constraint(model, c1, x <= -1)
#constraint(model, c2, 2 * x <= 1)
optimize!(model)
#assert termination_status(model) == MOI.INFEASIBLE
compute_conflict!(model)
julia> MOI.get(model, MOI.ConstraintConflictStatus(), LowerBoundRef(x))
IN_CONFLICT::ConflictParticipationStatusCode = 1
julia> MOI.get(model, MOI.ConstraintConflictStatus(), c1)
IN_CONFLICT::ConflictParticipationStatusCode = 1
julia> MOI.get(model, MOI.ConstraintConflictStatus(), c2)
NOT_IN_CONFLICT::ConflictParticipationStatusCode = 0