如何解决使用Julia JuMPGurobi计算不可还原的不一致子系统IIS
试图为我愚蠢的复杂模型计算IIS。
为清楚起见，我将包括整个模型：
torch-geometric尝试了几种方法，例如推荐的here和here，但遇到了错误。
尝试过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,Bin
L[Leave_week,Week],Bin
D[Dec_leave,Intern],Bin
s[Intern,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]
cap = @constraint(roster,[(b,d) in zip(rots,cap_rhs),k in 1:52],b] for i in Intern) <= d)
#dispensary
disp = @constraint(roster,[i in Intern],j] for k in Week,j in 14:18) >= 5)
disp1 = @constraint(roster,j] for k in 29:40,j in 14:18) >= 1)
disp2 = @constraint(roster,j] for k in 41:52,j in 14:18) >= 1)
clay_cap_o = @constraint(roster,[k in 1:4],14] for i in Intern) <=3)
clay_cap_o = @constraint(roster,[k in 5:52],14] for i in Intern) <=2)
#Orientation
IP_1 = @constraint(roster,1] for k in 1:6) >= 1)
IP_3_1 = @constraint(roster,sum(s[i,k] for k in 1:5) <= 1)
IP_3_11 = @constraint(roster,k] for i in Intern,k in 1:5) == 10)
IP_3_2 = @constraint(roster,[i in Intern,k in 1:4],x[i,1] == s[i,k])
# IP_lazy = @constraint(roster,[(i,k) in zip(Intern,[1 1 2 2 3 3 4 4 5 5 6])],1] ==1)
orien = @constraint(roster,j] for k in 1:4,j in [1,14,18] ) == 4)
orien1 = @constraint(roster,18]],j] for k in 1:4 ) <= 2)
#leave
# 2 weeks leave
@constraint(roster,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],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],j] for i in Intern) == rhs*L[l,k] )
@constraint(roster,max_leave[i in Intern],j] for j in 20:22,k in Week) ==2)
## - Dec_leave
@constraint(roster,sum(D[l,i] for l in 1:2) == 1)
@constraint(roster,[(l,d) in zip(1:2,5])],i] for i in Intern) == d)
@constraint(roster,(l,b) in zip(1:2,[49:50,51:52])],23] for k in b) == 2*D[l,i])
@constraint(roster,23] for k in Week) == 2)
#MIC
MIC_1_dvar = @constraint(roster,sum(y[i,4] for k in 5:27 ) == 1)
MIC_2_dvar = @constraint(roster,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,4]))
MIC = @constraint(roster,k in 29:51],4]))
#gen_med
g_vars = @constraint(roster,sum(g[i,m] for m in gen) ==1)
gen_duration_dvar = @constraint(roster,d) in zip(gen,7]),i in Intern],b] for k in 1:(52 - (d-1) ) ) == g[i,b])
gen_limit = @constraint(roster,b] for k in Week) == g[i,b]*d)
gen_durations = @constraint(roster,i in Intern,k in 1:(52 - (d-1) )],d - sum(x[i,b] for alpha in 0:(d-1) ) <= M*(1-y[i,b]))
ed_with_gen = @constraint(roster,k in 2:50],y[i,23] - x[i,k-1,8] - x[i,k+2,8] <= (1-g[i,5]))
#qum
qum_1 = @constraint(roster,13] for k in early) >= 1)
qum_2 = @constraint(roster,13] for k in 1:39) == 2)
# duration
dur_rot = [2,11,19]
durs = [2,2]
duration_dvar = @constraint(roster,d) in zip(dur_rot,durs),b] for k in 1:(52 - (d-1) ) ) == 1)
durations = @constraint(roster,b]))
AP_dur_var = @constraint(roster,3] for k in 5:35) == 1)
AP_dur = @constraint(roster,k in 5:35],3] for alpha in 0:1) <= M*(1 - y[i,3]))
AP_third = @constraint(roster,3] for k in 37:52) == 1)
# rotations_lengths
completion = @constraint(roster,[(j,c,d) in zip([1,19],[ 1:28,clins,non_clins,29:52,early,clins],[3,2]),j] for k in c) == d)
IP_soft = @constraint(roster,1] for k in Week) >= 5)
whole_year = @constraint(roster,[5,j] for k in Week) == d)
# public holiday constraints
no_pubs = @constraint(roster,k in [4,22,24,29,39,44,52],j in [12,13]],j] == 0 )
z = @expression(roster,j] for i in Intern,j in Rotation,k in Week))
obj_z = @objective(roster,Max,z)
optimize!(roster)
，但想出了
Gurobi.computeIIS(roster)任何帮助或建议，不胜感激。为了清楚起见，请随时编辑示例。
非常感谢。
解决方法
这有点先进，并且缺少一些管道（目前必须使用direct_model），但是您可以执行以下操作：
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,c1)
IN_CONFLICT::ConflictParticipationStatusCode = 1
julia> MOI.get(model,c2)
NOT_IN_CONFLICT::ConflictParticipationStatusCode = 0
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