gurobipy.GurobiError: Unable to convert argument to an expression

I am trying to make an optimize model, after running all restrictions and variables and realizing they are well defined, I get a problem trying to use

model.setObjective(objetivo, GRB.MINIMIZE)

it apears:

gurobipy.GurobiError: Unable to convert argument to an expression

here is my code:

import random

from gurobipy import GRB, Model, quicksum

random.seed(10)

# SUBINDICES:

N = 20

K = 3

T = 30

L = 22

# RANGOS:

Ingredientes = range(1, N+1)

Proovedores = range(1, K+1)

Tiempo = range(1, T+1)

Tiempo2 = range(2, T+1)

Tipo_de_sushi = range(1, L+1)

P = random.sample(Ingredientes, k=int(6))

I_sin_P = []

for i in Ingredientes:

if i not in P:

I_sin_P.append(i)

# PARAMETROS:

V = 4000000

W = 3300000

v = {(i): random.randint(100, 2500) for i in Ingredientes}

d = {(l, t): random.randint(0, 135) for l in Tipo_de_sushi for t in Tiempo}

q = 450

C = {(i): random.randint(2, 25) for i in Ingredientes}

p = {(i, k): random.randint(1000, 2000)

for i in Ingredientes for k in Proovedores}

a = {(i, l): random.randint(30, 50)

for i in Ingredientes for l in Tipo_de_sushi}

P = random.sample(Ingredientes, k=int(6))

I_sin_P = []

for i in Ingredientes:

if i not in P:

I_sin_P.append(i)

exp = {(i): random.randint(1, 20) for i in Ingredientes}

model = Model()

# VARIABLES:

x = model.addVars(Ingredientes, Tiempo, vtype=GRB.INTEGER, name="x_it")

r = model.addVars(Tipo_de_sushi, Tiempo, vtype=GRB.INTEGER, name="r_lt")

I = model.addVars(Ingredientes, exp, Tiempo,

vtype=GRB.INTEGER, name="I_igt")

s = model.addVars(Ingredientes, Tipo_de_sushi, exp, Tiempo,

vtype=GRB.INTEGER, name="s_ilgt")

o = model.addVars(P, Tiempo, vtype=GRB.INTEGER, name="o_pt")

F = model.addVars(Ingredientes, Tiempo, vtype=GRB.INTEGER, name="F_it")

h = model.addVars(Ingredientes, Tiempo, vtype=GRB.INTEGER, name="h_it")

u = model.addVars(Ingredientes, Tiempo, vtype=GRB.INTEGER, name="u_it")

model.update()

# RESTRICCIONES:

# R1

model.addConstrs(((r[l, t]) <= quicksum(s[i, l, e, t]/a[i, l] for e in range(2, exp[i]+1))

for t in Tiempo for l in Tipo_de_sushi for i in I_sin_P), name="R1")

# R2

model.addConstrs((r[l, t] <= d[l, t]

for t in Tiempo for l in Tipo_de_sushi), name="R2")

# R3

model.addConstrs(

(q >= quicksum(r[l, t] for l in Tipo_de_sushi) for t in Tiempo), name="R3")

# R4

model.addConstrs((I[i, exp[i], t] == x[i, t] - quicksum(s[i, l, exp[i], t] for l in Tipo_de_sushi)

for i in I_sin_P for t in Tiempo2), name="R4")

model.addConstrs((I[i, e, t] == I[i, e+1, t-1] - quicksum(s[i, l, e, t] for l in Tipo_de_sushi)

for i in I_sin_P for e in range(2, exp[i]-1) for t in Tiempo2), name="R5")

model.addConstrs((I[i, 1, t] >= I[i, 2, t-1] - quicksum(s[i, l, 1, t]

for l in Tipo_de_sushi) for i in I_sin_P for t in Tiempo2), name="R6")

model.addConstrs((I[i, e, 1] == C[i]

for i in I_sin_P for e in range(1, exp[i]+1)), name="R7")

# R5

model.addConstrs((F[p, t] == F[p, t-1] + x[p, t] - o[p, t-1]

for p in P for t in Tiempo2), name="R8")

model.addConstrs((F[p, 1] == C[p] + x[p, 1] for p in P), name="R11")

# R6

model.addConstrs((quicksum(p[i, k] for k in Proovedores) >= x[i, t]

for t in Tiempo for i in Ingredientes), name="R12")

# R7

model.addConstrs((quicksum(v[i]*quicksum(I[i, e, t] for e in range(2, exp[i])) for i in I_sin_P) <=

V for t in Tiempo), name="R12")

# R8

model.addConstrs((quicksum(v[p]*F[p, t]

for p in P) <= W for t in Tiempo), name="R13")

# R9

model.addConstrs((h[i, t] == I[i, 1, t]

for i in I_sin_P for t in Tiempo), name="R14")

# R10

model.addConstrs((u[p, t] == o[p, t-1] - quicksum(r[l, t]*a[p, l]

for l in Tipo_de_sushi) for p in P for t in Tiempo2), name="R15")

# R11

model.addConstrs((quicksum(d[l, t]*a[p, l] for l in Tipo_de_sushi)

<= o[p, t-1] for t in Tiempo2 for p in P), name="R16")

# R12

model.addConstrs((quicksum(a[i, l]*r[l, t] for l in Tipo_de_sushi) <= quicksum(I[i, e, t-1]

for e in range(2, exp[i])) for t in Tiempo2 for i in I_sin_P), name="R17")

# R13

model.addConstrs((o[p, t] <= F[p, t] for t in Tiempo for p in P), name="R18")

# R14

model.addConstrs((quicksum(r[l, t]

for l in Tipo_de_sushi) >= 250 for t in Tiempo))

# Naturaleza de las variables

model.addConstrs(

(x[i, t] >= 0 for t in Tiempo for i in Ingredientes), name="R19")

model.addConstrs(

(r[l, t] >= 0 for t in Tiempo for l in Tipo_de_sushi), name="R20")

model.addConstrs((I[i, e, t] >= 0 for t in Tiempo for i in Ingredientes for e in range(

1, exp[i])), name="R21")

model.addConstrs((s[i, l, e, t] >= 0 for t in Tiempo for i in Ingredientes for e in range(

1, exp[i]) for l in Tipo_de_sushi), name="R22")

model.addConstrs((o[p, t] >= 0 for t in Tiempo for p in P), name="R23")

model.addConstrs(

(F[i, t] >= 0 for i in Ingredientes for t in Tiempo), name="R24")

model.addConstrs(

(h[i, t] >= 0 for i in Ingredientes for t in Tiempo), name="R25")

model.addConstrs(

(u[i, t] >= 0 for i in Ingredientes for t in Tiempo), name="R26")

# Funcion Objetivo

objetivo = (quicksum(u[p, t] for p in P) + quicksum(h[i, t] for i in I_sin_P) + 0.01 * quicksum(

quicksum(r[l, t]*a[i, l] for i in Ingredientes) for l in Tipo_de_sushi) for t in Tiempo)

model.setObjective(objetivo, GRB.MINIMIZE)

model.optimize()

# Manejo Soluciones

print("\n"+"-"*10+" Manejo Soluciones "+"-"*10)

print(f"El valor objetivo es de: {model.ObjVal}")

正式なコメント

Simranjit Kaur

Gurobi Staff

2025年11月16日 23:26

This post is more than three years old. Some information may not be up to date. For current information, please check the Gurobi Documentation or Knowledge Base. If you need more help, please create a new post in the community forum, or try Gurobot, our chatbot interface offering instant, expert-level support.

Jonasz Staszek

Community Moderator

2022年10月17日 06:18

Dear Eduardo,

there is something wrong with your objective function. I intuitively tried to correct its implementation to:

objetivo = quicksum(quicksum(u[p, t] for p in P) + quicksum(h[i, t] for i in I_sin_P) + 0.01 * quicksum(
    quicksum(r[l, t]*a[i, l] for i in Ingredientes) for l in Tipo_de_sushi) for t in Tiempo)

but then your model is infeasible.

If that is not what you expect, you can share the mathematical formulation of your objective function. We may then be able to help you further.

Best regards
Jonasz

gurobipy.GurobiError: Unable to convert argument to an expression

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