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Help with implementing an LP

Ongoing
Lorenz Wagner
Investigator
Gurobi-versary
Conversationalist

Hello folks,

I need your help. I'm currently trying to implement an old LP (which I set up as part of my research at university) in a new programming language for nostalgic reasons. The old model was written in OLP, and now I want to implement it with Gurobi (Python). It is a manpower problem. I get the model set up without errors, but not solved, all solutions are infeasible. But I know that the model is feasible. Now I am wondering where I have made a mistake. I would be very grateful for any help.

You can find the relevant data here. In my code, I have read everything separately as CSV, but I upload the data in a .xlsx. You can find the old code here, the mathematical model here.

This is my current code:

import gurobipy as gp

from gurobipy import GRB

import pandas as pd




model = gp.Model()

T = [1,2,3,4,5,6,7]

B = [1,2]




# Data

df = pd.read_csv('/.../Work.csv', delimiter=';')

ID_W = df['Id'].tolist()

Q_W = df['Quali'].tolist()

WE_W = df['WE'].tolist()

NW_W = df['Night'].tolist()

F_W = df['Field'].tolist()

RT_W = df['Remaining Work Time'].tolist()

ED_W = df['ED'].tolist()

Q_W = {s:q for s,q in zip(ID_W, Q_W)}

WE_W = {s:we for s,we in zip(ID_W, WE_W)}

NW_W = {s:nd for s,nd in zip(ID_W, NW_W)}

F_W = {s:b for s,b in zip(ID_W, F_W)}

RT_W = {s:raz for s,raz in zip(ID_W, RT_W)}

ED_W = {s:na for s,na in zip(ID_W, ED_W)}

df1 = pd.read_csv('/.../People.csv', delimiter=';')

ID_P = df1['Id'].tolist()

C_P = df1['Cost'].tolist()

Q_P = df1['Quali'].tolist()

WT_P = df1['Work Time'].tolist()

ED_P = df1['ED'].tolist()

C_P = {a:c for a,c in zip(ID_P, C_P)}

Q_P = {a:q for a,q in zip(ID_P, Q_P)}

WT_P = {a:az for a,az in zip(ID_P, WT_P)}

ED_P = {a:na for a,na in zip(ID_P, ED_P)}




DemandList = [

[1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 0, 0],

[1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 0, 0],

[1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 1],

[0, 0, 0, 0, 0, 1, 1], [1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 0, 0],

[1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 1],

[0, 0, 0, 0, 0, 1, 1], [1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 0, 0],

[1, 0, 1, 0, 0, 0, 0]

]

Demanddict = {}

for i in ID_W:

for t in T:

Demanddict[i,t]=DemandList[i-1][t-1]




x = model.addVars(ID_P, ID_W, T, vtype=GRB.BINARY, name="x")

y = model.addVars(ID_P, B, vtype=GRB.BINARY, name="y")




# Objective function

model.setObjective(gp.quicksum(C_P[j] * y[j,b] for j in ID_P for b in B), GRB.MINIMIZE)




# Constraints

for j in ID_P:

for t in T:

for b in B:

model.addConstr((gp.quicksum(x[j,i,t] for i in F_W) <= y[j,b]), name = "CONS1")

for i in ID_W:

for t in T:

if Demanddict[i,t] > 0:

model.addConstr((gp.quicksum(x[j,i,t] for j in ID_P if Q_P[j] <= Q_W[i]) >= Demanddict[i,t]), name = "CONS3")

for j in ID_P:

for b in B:

model.addConstr((gp.quicksum(x[j,i,t] for i in ID_W for t in T if NW_W[i] == 1 and F_W[i] == b) <= 5), name = "CONS3")

for j in ID_P:

for b in B:

model.addConstr((gp.quicksum(x[j,i,t] for i in ID_W for t in T if WE_W[i] == 1 and F_W[i] == b) <= 2), name = "CONS4")

for j in ID_P:

for b in B:

model.addConstr((gp.quicksum(RT_W[i] * x[j,i,t] for i in ID_W for t in T if F_W[i] == b) <= WT_P[j] * y[j,b]), name = "CONS5")

for j in ID_P:

model.addConstr((gp.quicksum(y[j,b] for b in B) <= 1), name ="CONS6")

for t in T[:-1]:

for j in ID_P:

for i in ID_W:

if NW_W[i] == 1:

model.addConstr((x[j,i,t] + gp.quicksum(x[j,k,t+1] for k in ID_W if F_W[k] == F_W[i] and NW_W[k] != 1) <= 1), name = "CONS7")

model.optimize()
0

Comments

6 comments

Date Votes
  • Simranjit Kaur
    Gurobi Staff Gurobi Staff

    Have you tried to run the Model.ComputeIIS() method on the infeasible model? It will let you know the smallest set of variables and constraints that make your model infeasible. To this end, the following articles will be helpful:

    0
  • Lorenz Wagner
    Investigator
    Gurobi-versary
    Conversationalist

    Simranjit Kaur I just did. It yielded this output!

    Computing Irreducible Inconsistent Subsystem (IIS)...

               Constraints          |            Bounds           |  Runtime
          Min       Max     Guess   |   Min       Max     Guess   |
    --------------------------------------------------------------------------
            0      2031         -         0         0         0           0s
           31        31        31         0         0         0           1s

    IIS computed: 31 constraints, 0 bounds
    IIS runtime: 1.14 seconds (0.69 work units)
    Warning: linear constraint 0 and linear constraint 1 have the same name "CONS1"

    0
  • Simranjit Kaur
    Gurobi Staff Gurobi Staff
    • Edited

    Great! Once you've executed computeIIS(), the constraints participating in the IIS can be exported to an ILP file using model.write("mymodel.ilp"). You can inspect the constraints in this file using any text editor.

    0
  • Lorenz Wagner
    Investigator
    Gurobi-versary
    Conversationalist

    Thats the Output i get:

    \ Model _copy
    \ LP format - for model browsing. Use MPS format to capture full model detail.
    Minimize

    Subject To
    CONS1: x[1,1,5] + x[1,2,5] + x[1,3,5] + x[1,4,5] + x[1,5,5] + x[1,6,5]
    + x[1,7,5] + x[1,8,5] + x[1,9,5] + x[1,10,5] + x[1,11,5] + x[1,12,5]
    + x[1,13,5] + x[1,14,5] + x[1,15,5] + x[1,16,5] + x[1,17,5] + x[1,18,5]
    + x[1,19,5] - y[1,1] <= 0
    CONS1: x[1,1,5] + x[1,2,5] + x[1,3,5] + x[1,4,5] + x[1,5,5] + x[1,6,5]
    + x[1,7,5] + x[1,8,5] + x[1,9,5] + x[1,10,5] + x[1,11,5] + x[1,12,5]
    + x[1,13,5] + x[1,14,5] + x[1,15,5] + x[1,16,5] + x[1,17,5] + x[1,18,5]
    + x[1,19,5] - y[1,2] <= 0
    CONS1: x[2,1,5] + x[2,2,5] + x[2,3,5] + x[2,4,5] + x[2,5,5] + x[2,6,5]
    + x[2,7,5] + x[2,8,5] + x[2,9,5] + x[2,10,5] + x[2,11,5] + x[2,12,5]
    + x[2,13,5] + x[2,14,5] + x[2,15,5] + x[2,16,5] + x[2,17,5] + x[2,18,5]
    + x[2,19,5] - y[2,1] <= 0
    CONS1: x[3,1,5] + x[3,2,5] + x[3,3,5] + x[3,4,5] + x[3,5,5] + x[3,6,5]
    + x[3,7,5] + x[3,8,5] + x[3,9,5] + x[3,10,5] + x[3,11,5] + x[3,12,5]
    + x[3,13,5] + x[3,14,5] + x[3,15,5] + x[3,16,5] + x[3,17,5] + x[3,18,5]
    + x[3,19,5] - y[3,1] <= 0
    CONS1: x[4,1,5] + x[4,2,5] + x[4,3,5] + x[4,4,5] + x[4,5,5] + x[4,6,5]
    + x[4,7,5] + x[4,8,5] + x[4,9,5] + x[4,10,5] + x[4,11,5] + x[4,12,5]
    + x[4,13,5] + x[4,14,5] + x[4,15,5] + x[4,16,5] + x[4,17,5] + x[4,18,5]
    + x[4,19,5] - y[4,1] <= 0
    CONS1: x[4,1,5] + x[4,2,5] + x[4,3,5] + x[4,4,5] + x[4,5,5] + x[4,6,5]
    + x[4,7,5] + x[4,8,5] + x[4,9,5] + x[4,10,5] + x[4,11,5] + x[4,12,5]
    + x[4,13,5] + x[4,14,5] + x[4,15,5] + x[4,16,5] + x[4,17,5] + x[4,18,5]
    + x[4,19,5] - y[4,2] <= 0
    CONS1: x[5,1,5] + x[5,2,5] + x[5,3,5] + x[5,4,5] + x[5,5,5] + x[5,6,5]
    + x[5,7,5] + x[5,8,5] + x[5,9,5] + x[5,10,5] + x[5,11,5] + x[5,12,5]
    + x[5,13,5] + x[5,14,5] + x[5,15,5] + x[5,16,5] + x[5,17,5] + x[5,18,5]
    + x[5,19,5] - y[5,1] <= 0
    CONS1: x[5,1,5] + x[5,2,5] + x[5,3,5] + x[5,4,5] + x[5,5,5] + x[5,6,5]
    + x[5,7,5] + x[5,8,5] + x[5,9,5] + x[5,10,5] + x[5,11,5] + x[5,12,5]
    + x[5,13,5] + x[5,14,5] + x[5,15,5] + x[5,16,5] + x[5,17,5] + x[5,18,5]
    + x[5,19,5] - y[5,2] <= 0
    CONS1: x[6,1,5] + x[6,2,5] + x[6,3,5] + x[6,4,5] + x[6,5,5] + x[6,6,5]
    + x[6,7,5] + x[6,8,5] + x[6,9,5] + x[6,10,5] + x[6,11,5] + x[6,12,5]
    + x[6,13,5] + x[6,14,5] + x[6,15,5] + x[6,16,5] + x[6,17,5] + x[6,18,5]
    + x[6,19,5] - y[6,1] <= 0
    CONS1: x[6,1,5] + x[6,2,5] + x[6,3,5] + x[6,4,5] + x[6,5,5] + x[6,6,5]
    + x[6,7,5] + x[6,8,5] + x[6,9,5] + x[6,10,5] + x[6,11,5] + x[6,12,5]
    + x[6,13,5] + x[6,14,5] + x[6,15,5] + x[6,16,5] + x[6,17,5] + x[6,18,5]
    + x[6,19,5] - y[6,2] <= 0
    CONS1: x[7,1,5] + x[7,2,5] + x[7,3,5] + x[7,4,5] + x[7,5,5] + x[7,6,5]
    + x[7,7,5] + x[7,8,5] + x[7,9,5] + x[7,10,5] + x[7,11,5] + x[7,12,5]
    + x[7,13,5] + x[7,14,5] + x[7,15,5] + x[7,16,5] + x[7,17,5] + x[7,18,5]
    + x[7,19,5] - y[7,1] <= 0
    CONS1: x[8,1,5] + x[8,2,5] + x[8,3,5] + x[8,4,5] + x[8,5,5] + x[8,6,5]
    + x[8,7,5] + x[8,8,5] + x[8,9,5] + x[8,10,5] + x[8,11,5] + x[8,12,5]
    + x[8,13,5] + x[8,14,5] + x[8,15,5] + x[8,16,5] + x[8,17,5] + x[8,18,5]
    + x[8,19,5] - y[8,1] <= 0
    CONS1: x[9,1,5] + x[9,2,5] + x[9,3,5] + x[9,4,5] + x[9,5,5] + x[9,6,5]
    + x[9,7,5] + x[9,8,5] + x[9,9,5] + x[9,10,5] + x[9,11,5] + x[9,12,5]
    + x[9,13,5] + x[9,14,5] + x[9,15,5] + x[9,16,5] + x[9,17,5] + x[9,18,5]
    + x[9,19,5] - y[9,1] <= 0
    CONS1: x[9,1,5] + x[9,2,5] + x[9,3,5] + x[9,4,5] + x[9,5,5] + x[9,6,5]
    + x[9,7,5] + x[9,8,5] + x[9,9,5] + x[9,10,5] + x[9,11,5] + x[9,12,5]
    + x[9,13,5] + x[9,14,5] + x[9,15,5] + x[9,16,5] + x[9,17,5] + x[9,18,5]
    + x[9,19,5] - y[9,2] <= 0
    CONS1: x[10,1,5] + x[10,2,5] + x[10,3,5] + x[10,4,5] + x[10,5,5]
    + x[10,6,5] + x[10,7,5] + x[10,8,5] + x[10,9,5] + x[10,10,5]
    + x[10,11,5] + x[10,12,5] + x[10,13,5] + x[10,14,5] + x[10,15,5]
    + x[10,16,5] + x[10,17,5] + x[10,18,5] + x[10,19,5] - y[10,1] <= 0
    CONS3: x[1,11,5] + x[2,11,5] + x[3,11,5] + x[4,11,5] + x[5,11,5]
    + x[6,11,5] + x[7,11,5] + x[8,11,5] + x[9,11,5] + x[10,11,5] >= 1
    CONS5: 8 x[2,11,1] + 8 x[2,11,2] + 8 x[2,11,3] + 8 x[2,11,4] + 8 x[2,11,5]
    + 8 x[2,11,6] + 8 x[2,11,7] + 8 x[2,12,1] + 8 x[2,12,2] + 8 x[2,12,3]
    + 8 x[2,12,4] + 8 x[2,12,5] + 8 x[2,12,6] + 8 x[2,12,7] + 6 x[2,13,1]
    + 6 x[2,13,2] + 6 x[2,13,3] + 6 x[2,13,4] + 6 x[2,13,5] + 6 x[2,13,6]
    + 6 x[2,13,7] + 8 x[2,14,1] + 8 x[2,14,2] + 8 x[2,14,3] + 8 x[2,14,4]
    + 8 x[2,14,5] + 8 x[2,14,6] + 8 x[2,14,7] + 8 x[2,15,1] + 8 x[2,15,2]
    + 8 x[2,15,3] + 8 x[2,15,4] + 8 x[2,15,5] + 8 x[2,15,6] + 8 x[2,15,7]
    + 8 x[2,16,1] + 8 x[2,16,2] + 8 x[2,16,3] + 8 x[2,16,4] + 8 x[2,16,5]
    + 8 x[2,16,6] + 8 x[2,16,7] - 40 y[2,2] <= 0
    CONS5: 8 x[3,11,1] + 8 x[3,11,2] + 8 x[3,11,3] + 8 x[3,11,4] + 8 x[3,11,5]
    + 8 x[3,11,6] + 8 x[3,11,7] + 8 x[3,12,1] + 8 x[3,12,2] + 8 x[3,12,3]
    + 8 x[3,12,4] + 8 x[3,12,5] + 8 x[3,12,6] + 8 x[3,12,7] + 6 x[3,13,1]
    + 6 x[3,13,2] + 6 x[3,13,3] + 6 x[3,13,4] + 6 x[3,13,5] + 6 x[3,13,6]
    + 6 x[3,13,7] + 8 x[3,14,1] + 8 x[3,14,2] + 8 x[3,14,3] + 8 x[3,14,4]
    + 8 x[3,14,5] + 8 x[3,14,6] + 8 x[3,14,7] + 8 x[3,15,1] + 8 x[3,15,2]
    + 8 x[3,15,3] + 8 x[3,15,4] + 8 x[3,15,5] + 8 x[3,15,6] + 8 x[3,15,7]
    + 8 x[3,16,1] + 8 x[3,16,2] + 8 x[3,16,3] + 8 x[3,16,4] + 8 x[3,16,5]
    + 8 x[3,16,6] + 8 x[3,16,7] - 40 y[3,2] <= 0
    CONS5: 8 x[7,11,1] + 8 x[7,11,2] + 8 x[7,11,3] + 8 x[7,11,4] + 8 x[7,11,5]
    + 8 x[7,11,6] + 8 x[7,11,7] + 8 x[7,12,1] + 8 x[7,12,2] + 8 x[7,12,3]
    + 8 x[7,12,4] + 8 x[7,12,5] + 8 x[7,12,6] + 8 x[7,12,7] + 6 x[7,13,1]
    + 6 x[7,13,2] + 6 x[7,13,3] + 6 x[7,13,4] + 6 x[7,13,5] + 6 x[7,13,6]
    + 6 x[7,13,7] + 8 x[7,14,1] + 8 x[7,14,2] + 8 x[7,14,3] + 8 x[7,14,4]
    + 8 x[7,14,5] + 8 x[7,14,6] + 8 x[7,14,7] + 8 x[7,15,1] + 8 x[7,15,2]
    + 8 x[7,15,3] + 8 x[7,15,4] + 8 x[7,15,5] + 8 x[7,15,6] + 8 x[7,15,7]
    + 8 x[7,16,1] + 8 x[7,16,2] + 8 x[7,16,3] + 8 x[7,16,4] + 8 x[7,16,5]
    + 8 x[7,16,6] + 8 x[7,16,7] - 40 y[7,2] <= 0
    CONS5: 8 x[8,11,1] + 8 x[8,11,2] + 8 x[8,11,3] + 8 x[8,11,4] + 8 x[8,11,5]
    + 8 x[8,11,6] + 8 x[8,11,7] + 8 x[8,12,1] + 8 x[8,12,2] + 8 x[8,12,3]
    + 8 x[8,12,4] + 8 x[8,12,5] + 8 x[8,12,6] + 8 x[8,12,7] + 6 x[8,13,1]
    + 6 x[8,13,2] + 6 x[8,13,3] + 6 x[8,13,4] + 6 x[8,13,5] + 6 x[8,13,6]
    + 6 x[8,13,7] + 8 x[8,14,1] + 8 x[8,14,2] + 8 x[8,14,3] + 8 x[8,14,4]
    + 8 x[8,14,5] + 8 x[8,14,6] + 8 x[8,14,7] + 8 x[8,15,1] + 8 x[8,15,2]
    + 8 x[8,15,3] + 8 x[8,15,4] + 8 x[8,15,5] + 8 x[8,15,6] + 8 x[8,15,7]
    + 8 x[8,16,1] + 8 x[8,16,2] + 8 x[8,16,3] + 8 x[8,16,4] + 8 x[8,16,5]
    + 8 x[8,16,6] + 8 x[8,16,7] - 40 y[8,2] <= 0
    CONS5: 8 x[10,11,1] + 8 x[10,11,2] + 8 x[10,11,3] + 8 x[10,11,4]
    + 8 x[10,11,5] + 8 x[10,11,6] + 8 x[10,11,7] + 8 x[10,12,1]
    + 8 x[10,12,2] + 8 x[10,12,3] + 8 x[10,12,4] + 8 x[10,12,5]
    + 8 x[10,12,6] + 8 x[10,12,7] + 6 x[10,13,1] + 6 x[10,13,2]
    + 6 x[10,13,3] + 6 x[10,13,4] + 6 x[10,13,5] + 6 x[10,13,6]
    + 6 x[10,13,7] + 8 x[10,14,1] + 8 x[10,14,2] + 8 x[10,14,3]
    + 8 x[10,14,4] + 8 x[10,14,5] + 8 x[10,14,6] + 8 x[10,14,7]
    + 8 x[10,15,1] + 8 x[10,15,2] + 8 x[10,15,3] + 8 x[10,15,4]
    + 8 x[10,15,5] + 8 x[10,15,6] + 8 x[10,15,7] + 8 x[10,16,1]
    + 8 x[10,16,2] + 8 x[10,16,3] + 8 x[10,16,4] + 8 x[10,16,5]
    + 8 x[10,16,6] + 8 x[10,16,7] - 40 y[10,2] <= 0
    CONS6: y[1,1] + y[1,2] <= 1
    CONS6: y[2,1] + y[2,2] <= 1
    CONS6: y[3,1] + y[3,2] <= 1
    CONS6: y[4,1] + y[4,2] <= 1
    CONS6: y[5,1] + y[5,2] <= 1
    CONS6: y[6,1] + y[6,2] <= 1
    CONS6: y[7,1] + y[7,2] <= 1
    CONS6: y[8,1] + y[8,2] <= 1
    CONS6: y[9,1] + y[9,2] <= 1
    CONS6: y[10,1] + y[10,2] <= 1
    Bounds
    Binaries
    x[1,1,5] x[1,2,5] x[1,3,5] x[1,4,5] x[1,5,5] x[1,6,5] x[1,7,5] x[1,8,5]
    x[1,9,5] x[1,10,5] x[1,11,5] x[1,12,5] x[1,13,5] x[1,14,5] x[1,15,5]
    x[1,16,5] x[1,17,5] x[1,18,5] x[1,19,5] x[2,1,5] x[2,2,5] x[2,3,5]
    x[2,4,5] x[2,5,5] x[2,6,5] x[2,7,5] x[2,8,5] x[2,9,5] x[2,10,5] x[2,11,1]
    x[2,11,2] x[2,11,3] x[2,11,4] x[2,11,5] x[2,11,6] x[2,11,7] x[2,12,1]
    x[2,12,2] x[2,12,3] x[2,12,4] x[2,12,5] x[2,12,6] x[2,12,7] x[2,13,1]
    x[2,13,2] x[2,13,3] x[2,13,4] x[2,13,5] x[2,13,6] x[2,13,7] x[2,14,1]
    x[2,14,2] x[2,14,3] x[2,14,4] x[2,14,5] x[2,14,6] x[2,14,7] x[2,15,1]
    x[2,15,2] x[2,15,3] x[2,15,4] x[2,15,5] x[2,15,6] x[2,15,7] x[2,16,1]
    x[2,16,2] x[2,16,3] x[2,16,4] x[2,16,5] x[2,16,6] x[2,16,7] x[2,17,5]
    x[2,18,5] x[2,19,5] x[3,1,5] x[3,2,5] x[3,3,5] x[3,4,5] x[3,5,5] x[3,6,5]
    x[3,7,5] x[3,8,5] x[3,9,5] x[3,10,5] x[3,11,1] x[3,11,2] x[3,11,3]
    x[3,11,4] x[3,11,5] x[3,11,6] x[3,11,7] x[3,12,1] x[3,12,2] x[3,12,3]
    x[3,12,4] x[3,12,5] x[3,12,6] x[3,12,7] x[3,13,1] x[3,13,2] x[3,13,3]
    x[3,13,4] x[3,13,5] x[3,13,6] x[3,13,7] x[3,14,1] x[3,14,2] x[3,14,3]
    x[3,14,4] x[3,14,5] x[3,14,6] x[3,14,7] x[3,15,1] x[3,15,2] x[3,15,3]
    x[3,15,4] x[3,15,5] x[3,15,6] x[3,15,7] x[3,16,1] x[3,16,2] x[3,16,3]
    x[3,16,4] x[3,16,5] x[3,16,6] x[3,16,7] x[3,17,5] x[3,18,5] x[3,19,5]
    x[4,1,5] x[4,2,5] x[4,3,5] x[4,4,5] x[4,5,5] x[4,6,5] x[4,7,5] x[4,8,5]
    x[4,9,5] x[4,10,5] x[4,11,5] x[4,12,5] x[4,13,5] x[4,14,5] x[4,15,5]
    x[4,16,5] x[4,17,5] x[4,18,5] x[4,19,5] x[5,1,5] x[5,2,5] x[5,3,5]
    x[5,4,5] x[5,5,5] x[5,6,5] x[5,7,5] x[5,8,5] x[5,9,5] x[5,10,5] x[5,11,5]
    x[5,12,5] x[5,13,5] x[5,14,5] x[5,15,5] x[5,16,5] x[5,17,5] x[5,18,5]
    x[5,19,5] x[6,1,5] x[6,2,5] x[6,3,5] x[6,4,5] x[6,5,5] x[6,6,5] x[6,7,5]
    x[6,8,5] x[6,9,5] x[6,10,5] x[6,11,5] x[6,12,5] x[6,13,5] x[6,14,5]
    x[6,15,5] x[6,16,5] x[6,17,5] x[6,18,5] x[6,19,5] x[7,1,5] x[7,2,5]
    x[7,3,5] x[7,4,5] x[7,5,5] x[7,6,5] x[7,7,5] x[7,8,5] x[7,9,5] x[7,10,5]
    x[7,11,1] x[7,11,2] x[7,11,3] x[7,11,4] x[7,11,5] x[7,11,6] x[7,11,7]
    x[7,12,1] x[7,12,2] x[7,12,3] x[7,12,4] x[7,12,5] x[7,12,6] x[7,12,7]
    x[7,13,1] x[7,13,2] x[7,13,3] x[7,13,4] x[7,13,5] x[7,13,6] x[7,13,7]
    x[7,14,1] x[7,14,2] x[7,14,3] x[7,14,4] x[7,14,5] x[7,14,6] x[7,14,7]
    x[7,15,1] x[7,15,2] x[7,15,3] x[7,15,4] x[7,15,5] x[7,15,6] x[7,15,7]
    x[7,16,1] x[7,16,2] x[7,16,3] x[7,16,4] x[7,16,5] x[7,16,6] x[7,16,7]
    x[7,17,5] x[7,18,5] x[7,19,5] x[8,1,5] x[8,2,5] x[8,3,5] x[8,4,5] x[8,5,5]
    x[8,6,5] x[8,7,5] x[8,8,5] x[8,9,5] x[8,10,5] x[8,11,1] x[8,11,2]
    x[8,11,3] x[8,11,4] x[8,11,5] x[8,11,6] x[8,11,7] x[8,12,1] x[8,12,2]
    x[8,12,3] x[8,12,4] x[8,12,5] x[8,12,6] x[8,12,7] x[8,13,1] x[8,13,2]
    x[8,13,3] x[8,13,4] x[8,13,5] x[8,13,6] x[8,13,7] x[8,14,1] x[8,14,2]
    x[8,14,3] x[8,14,4] x[8,14,5] x[8,14,6] x[8,14,7] x[8,15,1] x[8,15,2]
    x[8,15,3] x[8,15,4] x[8,15,5] x[8,15,6] x[8,15,7] x[8,16,1] x[8,16,2]
    x[8,16,3] x[8,16,4] x[8,16,5] x[8,16,6] x[8,16,7] x[8,17,5] x[8,18,5]
    x[8,19,5] x[9,1,5] x[9,2,5] x[9,3,5] x[9,4,5] x[9,5,5] x[9,6,5] x[9,7,5]
    x[9,8,5] x[9,9,5] x[9,10,5] x[9,11,5] x[9,12,5] x[9,13,5] x[9,14,5]
    x[9,15,5] x[9,16,5] x[9,17,5] x[9,18,5] x[9,19,5] x[10,1,5] x[10,2,5]
    x[10,3,5] x[10,4,5] x[10,5,5] x[10,6,5] x[10,7,5] x[10,8,5] x[10,9,5]
    x[10,10,5] x[10,11,1] x[10,11,2] x[10,11,3] x[10,11,4] x[10,11,5]
    x[10,11,6] x[10,11,7] x[10,12,1] x[10,12,2] x[10,12,3] x[10,12,4]
    x[10,12,5] x[10,12,6] x[10,12,7] x[10,13,1] x[10,13,2] x[10,13,3]
    x[10,13,4] x[10,13,5] x[10,13,6] x[10,13,7] x[10,14,1] x[10,14,2]
    x[10,14,3] x[10,14,4] x[10,14,5] x[10,14,6] x[10,14,7] x[10,15,1]
    x[10,15,2] x[10,15,3] x[10,15,4] x[10,15,5] x[10,15,6] x[10,15,7]
    x[10,16,1] x[10,16,2] x[10,16,3] x[10,16,4] x[10,16,5] x[10,16,6]
    x[10,16,7] x[10,17,5] x[10,18,5] x[10,19,5] y[1,1] y[1,2] y[2,1] y[2,2]
    y[3,1] y[3,2] y[4,1] y[4,2] y[5,1] y[5,2] y[6,1] y[6,2] y[7,1] y[7,2]
    y[8,1] y[8,2] y[9,1] y[9,2] y[10,1] y[10,2]
    End
    0
  • Simranjit Kaur
    Gurobi Staff Gurobi Staff

    Hi Lorenz,

    The sum of the variables participating in the following constraint must be at least one:

    CONS3: x[1,11,5] + x[2,11,5] + x[3,11,5] + x[4,11,5] + x[5,11,5] + x[6,11,5] + x[7,11,5] + x[8,11,5] + x[9,11,5] + x[10,11,5] >= 1

    However, each of these variables is forced to zero from other constraints, which causes the infeasibility.

    To see this, let's look at the constraints in which x[1,11,5] participates.

    x[1,1,5] + x[1,2,5] + x[1,3,5] + x[1,4,5] + x[1,5,5] + x[1,6,5] + x[1,7,5] + x[1,8,5] + x[1,9,5] + x[1,10,5] + x[1,11,5] + x[1,12,5] + x[1,13,5] + x[1,14,5] + x[1,15,5] + x[1,16,5] + x[1,17,5] + x[1,18,5] + x[1,19,5] <= y[1,1] 
    x[1,1,5] + x[1,2,5] + x[1,3,5] + x[1,4,5] + x[1,5,5] + x[1,6,5] + x[1,7,5] + x[1,8,5] + x[1,9,5] + x[1,10,5] + x[1,11,5] + x[1,12,5] + x[1,13,5] + x[1,14,5] + x[1,15,5] + x[1,16,5] + x[1,17,5] + x[1,18,5] + x[1,19,5] <= y[1,2] 
    y[1,1] + y[1,2] <= 1

    The first two constraints imply that x[1,11,5] can be non-zero only if both y[1,2] and y[1,1] are positive. The third constraint forces at least one of y1,1] or y[1,2] to be zero, and therefore x[1,11,5] has to be zero. The same is true for other variables in the constraint CONS3 above.

    To resolve this, please check the data input for accuracy, review the logic behind these constraints, and ensure they are built as expected.

    Best regards,
    Simran

    0
  • Lorenz Wagner
    • Edited
    Investigator
    Gurobi-versary
    Conversationalist

    Thanks i got it. Sadly i dont really know why this happens. Do you have any idea which constraint may cause this issue or could be wrong?

    \[

    \begin{align} &\min\sum_{j,b}^{}j.C \cdot y_{jb}\\&\sum_{i:i.F}^{}x_{jit}\le y_{jf}~~~\forall j\in J,t\in T,f\in F\\&\sum_{j:j.Q\le i.Q}^{}x_{jit}\ge d_{it}~~~\forall i\in I,t\in T\\&\sum_{i,t:i.Night=1\wedge i.F=f}^{}x_{jit}\le 5~~~\forall j\in J,f\in F\\&\sum_{i,t:i.WE=1\wedge i.F=f}^{}x_{jit}\le 2~~~\forall j\in J,f\in F\\
    &\sum_{i,t:i.F=f}^{}i.RT\cdot x_{jit}\le j.WT\cdot y_{jf}~~~\forall j\in J,f\in F\\&\sum_{f\in F}^{}y_{jf}\le 1~~~~\forall j\in J\\&x_{jit}+\sum_{k:k.F=f\wedge k.Night\neq1}^{}x_{jk(t+1)}\le 1~~~\forall j\in J,t\in T,i:i.Night = 1\\\end{align}\]

    0

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