diff --git a/geco/mips/scheduling/generic.py b/geco/mips/scheduling/generic.py
index 75d00b9..b695be6 100644
--- a/geco/mips/scheduling/generic.py
+++ b/geco/mips/scheduling/generic.py
@@ -58,10 +58,11 @@ def generate_params(number_of_facilities, number_of_tasks, seed=0):
 
     release_times = [0] * number_of_tasks
 
-    deadlines = {}
     beta = 20 / 9
-    for j in range(number_of_tasks):
-        deadlines[j] = seed.uniform(beta * number_of_tasks / 4, beta * number_of_tasks)
+    deadlines = [
+        seed.uniform(beta * number_of_tasks / 4, beta * number_of_tasks)
+        for _ in range(number_of_tasks)
+    ]
 
     resource_requirements = {}
     for j, k in itertools.product(range(number_of_tasks), range(number_of_facilities)):
diff --git a/geco/mips/scheduling/heinz.py b/geco/mips/scheduling/heinz.py
index 904f0b4..8e7a8d0 100644
--- a/geco/mips/scheduling/heinz.py
+++ b/geco/mips/scheduling/heinz.py
@@ -1,7 +1,5 @@
-import itertools
-
 import pyscipopt as scip
-from networkx.utils import py_random_state
+
 from geco.mips.scheduling.generic import *
 
 
@@ -119,7 +117,7 @@ def heinz_formulation(
     """
     model = scip.Model(name)
 
-    time_steps = range(min(release_dates), int(max(deadlines.values())))
+    time_steps = range(min(release_dates), int(max(deadlines)))
 
     # objective function
     x = {}
@@ -170,7 +168,7 @@ def heinz_formulation(
 
     # constraint (15)
     epsilon = filter(
-        lambda ts: ts[0] < ts[1], itertools.product(release_dates, deadlines.values())
+        lambda ts: ts[0] < ts[1], itertools.product(release_dates, deadlines)
     )
     for k, (t1, t2) in itertools.product(range(number_of_facilities), epsilon):
         model.addCons(
diff --git a/geco/mips/tests/test_scheduling.py b/geco/mips/tests/test_scheduling.py
index 0c6b5f4..20a37a9 100644
--- a/geco/mips/tests/test_scheduling.py
+++ b/geco/mips/tests/test_scheduling.py
@@ -22,7 +22,7 @@ def test_hooker_generation():
 def test_heinz_formulation(number_of_facilities, number_of_tasks, seed):
     main_params = number_of_facilities, number_of_tasks
     p, C, c, R, d, r = heinz_params(*main_params, seed)
-    time_steps = int(max(d.values()) - min(R))
+    time_steps = int(max(d) - min(R))
     model = heinz_instance(*main_params)
     x_vars_count = number_of_facilities * number_of_tasks
     y_vars_lowerbound = 0
@@ -75,7 +75,7 @@ def test_hooker_simple_instance():
 
 
 def test_heinz_simple_instance():
-    (
+    params = (
         n_resources,
         n_tasks,
         time_steps,
@@ -112,7 +112,7 @@ def _simple_instance_params():
     release_times = [0]
     # change the deadline to 0 or 1 to make it infeasible for the heinz formulation
     # and add 1 late task to the hooker formulation
-    deadlines = {0: 2}
+    deadlines = [2]
     resource_requirements = {(0, 0): 1}
     return (
         n_resources,