diff --git a/ema_workbench/analysis/cart.py b/ema_workbench/analysis/cart.py
index a9fcf6f01..7b3b81fb3 100644
--- a/ema_workbench/analysis/cart.py
+++ b/ema_workbench/analysis/cart.py
@@ -5,6 +5,8 @@
 
 """
 
+from __future__ import annotations
+
 import contextlib
 import io
 import math
@@ -13,6 +15,7 @@
 import matplotlib.image as mpimg
 import matplotlib.pyplot as plt
 import numpy as np
+import numpy.typing as npt
 import pandas as pd
 from sklearn import tree
 
@@ -71,7 +74,13 @@ class CART(sdutil.OutputFormatterMixin):
 
     sep = "!?!"
 
-    def __init__(self, x, y, mass_min=0.05, mode=sdutil.RuleInductionType.BINARY):
+    def __init__(
+        self,
+        x: pd.DataFrame,
+        y: npt.NDArray,
+        mass_min: float = 0.05,
+        mode: sdutil.RuleInductionType = sdutil.RuleInductionType.BINARY,
+    ) -> None:
         """Init."""
         with contextlib.suppress(KeyError):
             x = x.drop(["scenario"], axis=1)
@@ -97,7 +106,7 @@ def __init__(self, x, y, mass_min=0.05, mode=sdutil.RuleInductionType.BINARY):
         self._stats = None
 
     @property
-    def boxes(self):
+    def boxes(self) -> list[pd.DataFrame]:
         """Return a list with the box limits for each terminal leaf.
 
         Returns
@@ -176,7 +185,7 @@ def recurse(left, right, child, lineage=None):
         return self._boxes
 
     @property
-    def stats(self):
+    def stats(self) -> list[dict]:
         """Returns list with the scenario discovery statistics for each terminal leaf.
 
         Returns
@@ -197,7 +206,7 @@ def stats(self):
             self._stats.append(boxstats)
         return self._stats
 
-    def _binary_stats(self, box, box_init):
+    def _binary_stats(self, box: pd.DataFrame, box_init: pd.DataFrame) -> dict:
         indices = sdutil._in_box(self.x, box)
 
         y_in_box = self.y[indices]
@@ -211,7 +220,7 @@ def _binary_stats(self, box, box_init):
         }
         return boxstats
 
-    def _regression_stats(self, box, box_init):
+    def _regression_stats(self, box: pd.DataFrame, box_init: pd.DataFrame) -> dict:
         indices = sdutil._in_box(self.x, box)
 
         y_in_box = self.y[indices]
@@ -223,7 +232,7 @@ def _regression_stats(self, box, box_init):
         }
         return boxstats
 
-    def _classification_stats(self, box, box_init):
+    def _classification_stats(self, box: pd.DataFrame, box_init: pd.DataFrame) -> dict:
         indices = sdutil._in_box(self.x, box)
 
         y_in_box = self.y[indices]
@@ -252,7 +261,7 @@ def _classification_stats(self, box, box_init):
         sdutil.RuleInductionType.CLASSIFICATION: _classification_stats,
     }
 
-    def build_tree(self):
+    def build_tree(self) -> None:
         """Train CART on the data."""
         min_samples = int(self.mass_min * self.x.shape[0])
 
@@ -262,7 +271,7 @@ def build_tree(self):
             self.clf = tree.DecisionTreeClassifier(min_samples_leaf=min_samples)
         self.clf.fit(self._x, self.y)
 
-    def show_tree(self, mplfig=True, format="png"):
+    def show_tree(self, mplfig: bool = True, format: str = "png"):
         """Return a png (defaults) or svg of the tree.
 
         On Windows, graphviz needs to be installed with conda.
diff --git a/ema_workbench/analysis/pairs_plotting.py b/ema_workbench/analysis/pairs_plotting.py
index 0f1dc8d84..fcef55b7e 100644
--- a/ema_workbench/analysis/pairs_plotting.py
+++ b/ema_workbench/analysis/pairs_plotting.py
@@ -1,9 +1,13 @@
 """R-style pairs plotting functionality."""
 
+from __future__ import annotations
+
+import matplotlib.axes
 import matplotlib.cm as cm
 import matplotlib.gridspec as gridspec
 import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 
 from ..util import get_module_logger
 
@@ -17,15 +21,15 @@
 
 
 def pairs_lines(
-    experiments,
-    outcomes,
-    outcomes_to_show=None,
-    group_by=None,
+    experiments: pd.DataFrame,
+    outcomes: dict[str, np.ndarray],
+    outcomes_to_show: list[str] | None = None,
+    group_by: str | None = None,
     grouping_specifiers=None,
-    ylabels=None,
-    legend=True,
+    ylabels: dict[str, str] | None = None,
+    legend: bool = True,
     **kwargs,
-):
+) -> tuple[plt.Figure, dict[str, matplotlib.axes.Axes]]:
     """Generate a pairs lines multiplot.
 
     It shows the behavior of two outcomes over time against
@@ -147,18 +151,18 @@ def simple_pairs_lines(ax, y_data, x_data, color):
 
 
 def pairs_density(
-    experiments,
-    outcomes,
-    outcomes_to_show=None,
-    group_by=None,
+    experiments: pd.DataFrame,
+    outcomes: dict[str, np.ndarray],
+    outcomes_to_show: list[str] | None = None,
+    group_by: str | None = None,
     grouping_specifiers=None,
-    ylabels=None,
-    point_in_time=-1,
-    log=True,
-    gridsize=50,
-    colormap="coolwarm",
-    filter_scalar=True,
-):
+    ylabels: dict[str, str] | None = None,
+    point_in_time: int = -1,
+    log: bool = True,
+    gridsize: int = 50,
+    colormap: str = "coolwarm",
+    filter_scalar: bool = True,
+) -> tuple[plt.Figure, dict[str, matplotlib.axes.Axes]]:
     """Generate a pairs hexbin density multiplot.
 
     In case of time-series data, the end states are used.
@@ -392,17 +396,17 @@ def simple_pairs_density(
 
 
 def pairs_scatter(
-    experiments,
-    outcomes,
-    outcomes_to_show=None,
-    group_by=None,
+    experiments: pd.DataFrame,
+    outcomes: dict[str, np.ndarray],
+    outcomes_to_show: list[str] | None = None,
+    group_by: str | None = None,
     grouping_specifiers=None,
-    ylabels=None,
-    legend=True,
-    point_in_time=-1,
-    filter_scalar=False,
+    ylabels: dict[str, str] | None = None,
+    legend: bool = True,
+    point_in_time: int = -1,
+    filter_scalar: bool = False,
     **kwargs,
-):
+) -> tuple[plt.Figure, dict[str, matplotlib.axes.Axes]]:
     """Generate a pairs scatter multiplot.
 
     In case of time-series data, the end states are used.
diff --git a/ema_workbench/analysis/parcoords.py b/ema_workbench/analysis/parcoords.py
index f88cb9030..6ac886b42 100644
--- a/ema_workbench/analysis/parcoords.py
+++ b/ema_workbench/analysis/parcoords.py
@@ -1,5 +1,7 @@
 """A general purpose matplotlib-based parallel coordinate plotting Class."""
 
+from __future__ import annotations
+
 import matplotlib.pyplot as plt
 import matplotlib.ticker as ticker
 import pandas as pd
@@ -14,7 +16,7 @@
 __all__ = ["ParallelAxes", "get_limits"]
 
 
-def setup_parallel_plot(labels, minima, maxima, formatter=None, fs=14, rot=90):
+def setup_parallel_plot(labels: list[str], minima: pd.Series, maxima: pd.Series, formatter: dict[str, str] | None = None, fs: int = 14, rot: float = 90) -> tuple[plt.Figure, list[plt.Axes], dict]:
     """Helper function for setting up the parallel axes plot.
 
     Parameters
@@ -100,7 +102,7 @@ def setup_parallel_plot(labels, minima, maxima, formatter=None, fs=14, rot=90):
     return fig, axes, tick_labels
 
 
-def get_limits(data):
+def get_limits(data: pd.DataFrame) -> pd.DataFrame:
     """Helper function to get limits of a FataFrame that can serve as input to ParallelAxis.
 
     Parameters
@@ -165,7 +167,7 @@ class ParallelAxes:
 
     """
 
-    def __init__(self, limits, formatter=None, fontsize=14, rot=90):
+    def __init__(self, limits: pd.DataFrame, formatter: dict[str, str] | None = None, fontsize: int = 14, rot: float = 90):
         """Init.
 
         Parameters
@@ -216,7 +218,7 @@ def __init__(self, limits, formatter=None, fontsize=14, rot=90):
         plt.tight_layout(h_pad=0, w_pad=0)
         plt.subplots_adjust(wspace=0)
 
-    def plot(self, data, color=None, label=None, **kwargs):
+    def plot(self, data: pd.DataFrame | pd.Series, color=None, label: str | None = None, **kwargs) -> None:
         """Plot data on parallel axes.
 
         Parameters
@@ -257,7 +259,7 @@ def plot(self, data, color=None, label=None, **kwargs):
         # plot the data
         self._plot(normalized_data, color=color, **kwargs)
 
-    def legend(self):
+    def legend(self) -> None:
         """Add a legend to the figure."""
         artists = []
         labels = []
@@ -299,7 +301,7 @@ def _plot(self, data, **kwargs):
             if label_j in self.flipped_axes:
                 self._update_plot_data(ax, 1, lines=lines)
 
-    def invert_axis(self, axis):
+    def invert_axis(self, axis: str | list[str]) -> None:
         """Flip direction for specified axis.
 
         Parameters
diff --git a/ema_workbench/analysis/plotting.py b/ema_workbench/analysis/plotting.py
index b8796cc13..fb2ecd0d5 100644
--- a/ema_workbench/analysis/plotting.py
+++ b/ema_workbench/analysis/plotting.py
@@ -6,6 +6,7 @@
 
 import matplotlib.pyplot as plt
 import numpy as np
+import pandas as pd
 from matplotlib.patches import ConnectionPatch
 
 from ..util import EMAError, get_module_logger
@@ -36,18 +37,18 @@
 
 
 def envelopes(
-    experiments,
-    outcomes,
-    outcomes_to_show=None,
-    group_by=None,
+    experiments: pd.DataFrame,
+    outcomes: dict[str, np.ndarray],
+    outcomes_to_show: str | list[str] | None = None,
+    group_by: str | None = None,
     grouping_specifiers=None,
-    density=None,
-    fill=False,
-    legend=True,
-    titles=None,
-    ylabels=None,
-    log=False,
-):
+    density: Density | None = None,
+    fill: bool = False,
+    legend: bool = True,
+    titles: dict[str, str] | None = None,
+    ylabels: dict[str, str] | None = None,
+    log: bool = False,
+) -> tuple[plt.Figure, dict[str, plt.Axes]]:
     """Make envelop plots.
 
     An envelope shows over time the minimum and maximum  value for a set
@@ -260,19 +261,19 @@ def single_envelope(outcomes, outcome_to_plot, time, density, ax, ax_d, fill, lo
 
 
 def lines(
-    experiments,
-    outcomes,
-    outcomes_to_show=None,
-    group_by=None,
+    experiments: pd.DataFrame,
+    outcomes: dict[str, np.ndarray],
+    outcomes_to_show: str | list[str] | None = None,
+    group_by: str | None = None,
     grouping_specifiers=None,
-    density="",
-    legend=True,
-    titles=None,
-    ylabels=None,
-    experiments_to_show=None,
-    show_envelope=False,
-    log=False,
-):
+    density: Density | str = "",
+    legend: bool = True,
+    titles: dict[str, str] | None = None,
+    ylabels: dict[str, str] | None = None,
+    experiments_to_show: np.ndarray | None = None,
+    show_envelope: bool = False,
+    log: bool = False,
+) -> tuple[plt.Figure, dict[str, plt.Axes]]:
     """Visualize results from experiments as line plots.
 
     It is thus to be used in case of time
@@ -601,13 +602,13 @@ def simple_lines(outcomes, outcome_to_plot, time, density, ax, ax_d, log):
 
 
 def kde_over_time(
-    experiments,
-    outcomes,
-    outcomes_to_show=None,
-    group_by=None,
+    experiments: pd.DataFrame,
+    outcomes: dict[str, np.ndarray],
+    outcomes_to_show: str | list[str] | None = None,
+    group_by: str | None = None,
     grouping_specifiers=None,
-    colormap="viridis",
-    log=True,
+    colormap: str = "viridis",
+    log: bool = True,
 ):
     """Plot a KDE over time. The KDE is visualized through a heatmap.
 
@@ -679,21 +680,21 @@ def kde_over_time(
 
 
 def multiple_densities(
-    experiments,
-    outcomes,
-    points_in_time=None,
-    outcomes_to_show=None,
-    group_by=None,
+    experiments: pd.DataFrame,
+    outcomes: dict[str, np.ndarray],
+    points_in_time: list[float] | None = None,
+    outcomes_to_show: str | list[str] | None = None,
+    group_by: str | None = None,
     grouping_specifiers=None,
-    density=Density.KDE,
-    legend=True,
-    titles=None,
-    ylabels=None,
-    experiments_to_show=None,
-    plot_type=PlotType.ENVELOPE,
-    log=False,
+    density: Density = Density.KDE,
+    legend: bool = True,
+    titles: dict[str, str] | None = None,
+    ylabels: dict[str, str] | None = None,
+    experiments_to_show: np.ndarray | None = None,
+    plot_type: PlotType = PlotType.ENVELOPE,
+    log: bool = False,
     **kwargs,
-):
+) -> tuple[list[plt.Figure], dict[str, dict[str, plt.Axes]]]:
     """Make an envelope plot with multiple density plots over the run time.
 
     Parameters
diff --git a/ema_workbench/analysis/plotting_util.py b/ema_workbench/analysis/plotting_util.py
index b782cd310..de913f911 100644
--- a/ema_workbench/analysis/plotting_util.py
+++ b/ema_workbench/analysis/plotting_util.py
@@ -1,12 +1,18 @@
 """Plotting utility functions."""
 
+from __future__ import annotations
+
 import copy
 import enum
+from typing import Any
 
 import matplotlib as mpl
+import matplotlib.axes
+import matplotlib.figure
 import matplotlib.gridspec as gridspec
 import matplotlib.pyplot as plt
 import numpy as np
+import numpy.typing as npt
 import pandas as pd
 import scipy.stats as stats
 import seaborn as sns
@@ -74,7 +80,7 @@ class PlotType(enum.Enum):
     """constant for plotting envelopes with lines."""
 
 
-def plot_envelope(ax, j, time, value, fill=False):
+def plot_envelope(ax: matplotlib.axes.Axes, j: int, time: npt.NDArray, value: npt.NDArray, fill: bool = False) -> None:
     """Helper function, responsible for plotting an envelope.
 
     Parameters
@@ -102,7 +108,7 @@ def plot_envelope(ax, j, time, value, fill=False):
         ax.plot(time, maximum, c=color)
 
 
-def plot_histogram(ax, values, log):
+def plot_histogram(ax: matplotlib.axes.Axes, values: npt.NDArray | list[npt.NDArray], log: bool) -> Any:
     """Helper function, responsible for plotting a histogram.
 
     Parameters
@@ -131,7 +137,7 @@ def plot_histogram(ax, values, log):
     return a
 
 
-def plot_kde(ax, values, log):
+def plot_kde(ax: matplotlib.axes.Axes, values: list[npt.NDArray], log: bool) -> None:
     """Helper function, responsible for plotting a KDE.
 
     Parameters
@@ -155,7 +161,7 @@ def plot_kde(ax, values, log):
             ax.set_xticklabels(labels)
 
 
-def plot_boxplots(ax, values, log, group_labels=None):
+def plot_boxplots(ax: matplotlib.axes.Axes, values: list[npt.NDArray], log: bool, group_labels: list[str] | None = None) -> None:
     """Helper function for plotting a boxplot.
 
     Parameters
@@ -189,7 +195,7 @@ def plot_boxplots(ax, values, log, group_labels=None):
     sns.boxplot(x="id_var", y=0, data=data, order=group_labels, ax=ax)
 
 
-def plot_violinplot(ax, values, log, group_labels=None):
+def plot_violinplot(ax: matplotlib.axes.Axes, values: list[npt.NDArray], log: bool, group_labels: list[str] | None = None) -> None:
     """Helper function for plotting violin plots on axes.
 
     Parameters
@@ -214,7 +220,7 @@ def plot_violinplot(ax, values, log, group_labels=None):
     sns.violinplot(x="variable", y="value", data=data, order=group_labels, ax=ax)
 
 
-def plot_boxenplot(ax, values, log, group_labels=None):
+def plot_boxenplot(ax: matplotlib.axes.Axes, values: list[npt.NDArray], log: bool, group_labels: list[str] | None = None) -> None:
     """Helper function for plotting boxenplot plots on axes.
 
     Parameters
@@ -237,8 +243,8 @@ def plot_boxenplot(ax, values, log, group_labels=None):
 
 
 def group_density(
-    ax_d, density, outcomes, outcome_to_plot, group_labels, log=False, index=-1
-):
+    ax_d: matplotlib.axes.Axes, density: Density, outcomes: dict, outcome_to_plot: str, group_labels: list[str], log: bool = False, index: int = -1
+) -> None:
     """Helper function for plotting densities in case of grouped data.
 
     Parameters
@@ -276,7 +282,7 @@ def group_density(
     ax_d.set_ylabel("")
 
 
-def simple_density(density, value, ax_d, ax, log):
+def simple_density(density: Density, value: npt.NDArray, ax_d: matplotlib.axes.Axes, ax: matplotlib.axes.Axes, log: bool) -> None:
     """Helper function, responsible for producing a density plot.
 
     Parameters
@@ -313,7 +319,7 @@ def simple_density(density, value, ax_d, ax, log):
     ax_d.set_ylabel("")
 
 
-def simple_kde(outcomes, outcomes_to_show, colormap, log, minima, maxima):
+def simple_kde(outcomes: dict[str, npt.NDArray], outcomes_to_show: list[str], colormap: str, log: bool, minima: dict[str, float], maxima: dict[str, float]) -> tuple[matplotlib.figure.Figure, dict[str, matplotlib.axes.Axes]]:
     """Helper function for generating a density heatmap over time.
 
     Parameters
@@ -360,7 +366,7 @@ def simple_kde(outcomes, outcomes_to_show, colormap, log, minima, maxima):
     return fig, axes_dict
 
 
-def make_legend(categories, ax, ncol=3, legend_type=LegendEnum.LINE, alpha=1):
+def make_legend(categories: list[str], ax: matplotlib.axes.Axes, ncol: int = 3, legend_type: LegendEnum = LegendEnum.LINE, alpha: float = 1) -> None:
     """Helper function responsible for making the legend.
 
     Parameters
@@ -423,7 +429,7 @@ def make_legend(categories, ax, ncol=3, legend_type=LegendEnum.LINE, alpha=1):
     )
 
 
-def determine_kde(data, size_kde=1000, ymin=None, ymax=None):
+def determine_kde(data: npt.NDArray, size_kde: int = 1000, ymin: float | None = None, ymax: float | None = None) -> tuple[npt.NDArray, npt.NDArray]:
     """Helper function responsible for performing a KDE.
 
     Parameters
@@ -468,7 +474,7 @@ def determine_kde(data, size_kde=1000, ymin=None, ymax=None):
     return kde_x, kde_y
 
 
-def filter_scalar_outcomes(outcomes):
+def filter_scalar_outcomes(outcomes: dict[str, npt.NDArray]) -> dict[str, npt.NDArray]:
     """Helper function that removes non time series outcomes from all the utcomes.
 
     Parameters
@@ -491,7 +497,7 @@ def filter_scalar_outcomes(outcomes):
     return temp
 
 
-def determine_time_dimension(outcomes):
+def determine_time_dimension(outcomes: dict[str, npt.NDArray]) -> tuple[npt.NDArray | None, dict[str, npt.NDArray]]:
     """Helper function for determining or creating time dimension.
 
     Parameters
@@ -522,8 +528,8 @@ def determine_time_dimension(outcomes):
 
 
 def group_results(
-    experiments, outcomes, group_by, grouping_specifiers, grouping_labels
-):
+    experiments: pd.DataFrame, outcomes: dict[str, npt.NDArray], group_by: str, grouping_specifiers, grouping_labels: list[str]
+) -> dict:
     """Helper function that takes the experiments and results and returns a list based on groupoing.
 
     Each element in the dictionary contains the experiments
@@ -596,7 +602,7 @@ def group_results(
     return groups
 
 
-def make_continuous_grouping_specifiers(array, nr_of_groups=5):
+def make_continuous_grouping_specifiers(array: npt.NDArray, nr_of_groups: int = 5) -> list[tuple[float, float]]:
     """Helper function for discretizing a continuous array.
 
     By default, the array is split into 5 equally wide intervals.
@@ -630,13 +636,13 @@ def make_continuous_grouping_specifiers(array, nr_of_groups=5):
 
 
 def prepare_pairs_data(
-    experiments,
-    outcomes,
-    outcomes_to_show=None,
-    group_by=None,
+    experiments: pd.DataFrame,
+    outcomes: dict[str, npt.NDArray],
+    outcomes_to_show: list[str] | None = None,
+    group_by: str | None = None,
     grouping_specifiers=None,
-    point_in_time=-1,
-    filter_scalar=True,
+    point_in_time: int = -1,
+    filter_scalar: bool = True,
 ):
     """Helper function to prepare the data for pairs plotting.
 
@@ -696,13 +702,13 @@ def filter_outcomes(outcomes, point_in_time):
 
 
 def prepare_data(
-    experiments,
-    experiments_to_show,
-    outcomes,
-    outcomes_to_show=None,
-    group_by=None,
+    experiments: pd.DataFrame,
+    experiments_to_show: npt.NDArray | None,
+    outcomes: dict[str, npt.NDArray],
+    outcomes_to_show: str | list[str] | None = None,
+    group_by: str | None = None,
     grouping_specifiers=None,
-    filter_scalar=True,
+    filter_scalar: bool = True,
 ):
     """Helper function for preparing datasets prior to plotting.
 
@@ -787,7 +793,7 @@ def prepare_data(
     return experiments, outcomes, outcomes_to_show, time, grouping_labels
 
 
-def do_titles(ax, titles, outcome):
+def do_titles(ax: matplotlib.axes.Axes, titles: dict[str, str] | None, outcome: str) -> None:
     """Helper function for setting the title on an ax.
 
     Parameters
@@ -812,7 +818,7 @@ def do_titles(ax, titles, outcome):
                 ax.set_title(outcome)
 
 
-def do_ylabels(ax, ylabels, outcome):
+def do_ylabels(ax: matplotlib.axes.Axes, ylabels: dict[str, str] | None, outcome: str) -> None:
     """Helper function for setting the y labels on an ax.
 
     Parameters
@@ -837,7 +843,7 @@ def do_ylabels(ax, ylabels, outcome):
                 ax.set_ylabel(outcome)
 
 
-def make_grid(outcomes_to_show, density=False):
+def make_grid(outcomes_to_show: list[str], density: bool = False) -> tuple[matplotlib.figure.Figure, gridspec.GridSpec]:
     """Helper function for making the grid that specifies the size and location of all axes.
 
     Parameters
@@ -858,7 +864,7 @@ def make_grid(outcomes_to_show, density=False):
     return figure, grid
 
 
-def get_color(index):
+def get_color(index: int):
     """Helper function for cycling over color list.
 
     Useful if the number of items is higher than the length of the color list.
diff --git a/ema_workbench/analysis/prim.py b/ema_workbench/analysis/prim.py
index 17adfadb7..5aadc068d 100644
--- a/ema_workbench/analysis/prim.py
+++ b/ema_workbench/analysis/prim.py
@@ -18,7 +18,7 @@
 import itertools
 import numbers
 import warnings
-from collections.abc import Iterable, Sequence
+from collections.abc import Iterable
 from operator import itemgetter
 from typing import Literal
 
@@ -38,6 +38,7 @@
         "altair based interactive inspection not available", ImportWarning, stacklevel=2
     )
 
+from ..em_framework.util import NumpySeedLike, RNGLike
 from ..util import INFO, EMAError, get_module_logger, temporary_filter
 from . import scenario_discovery_util as sdutil
 from .prim_util import (
@@ -57,9 +58,6 @@
 #
 # .. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
 
-SeedLike = int | np.integer | Sequence[int] | np.random.SeedSequence
-RNGLike = np.random.Generator | np.random.BitGenerator
-
 
 __all__ = [
     "PRIMObjectiveFunctions",
@@ -828,7 +826,7 @@ def resample(
         i: int | None = None,
         iterations: int = 10,
         p: float = 1 / 2,
-        rng: RNGLike | SeedLike | None = None,
+        rng: RNGLike | NumpySeedLike | None = None,
     ) -> pd.DataFrame:
         """Calculate resample statistics for candidate box i.
 
diff --git a/ema_workbench/connectors/excel.py b/ema_workbench/connectors/excel.py
index f30851b1d..c78d2c0ad 100644
--- a/ema_workbench/connectors/excel.py
+++ b/ema_workbench/connectors/excel.py
@@ -69,8 +69,8 @@ class BaseExcelModel(FileModel):
     com_warning_msg = "com error: no cell(s) named %s found"
 
     def __init__(
-        self, name, wd=None, model_file=None, default_sheet=None, pointers=None
-    ):
+        self, name: str, wd: str | None = None, model_file: str | None = None, default_sheet: str | None = None, pointers: dict[str, str] | None = None
+    ) -> None:
         super().__init__(name, wd=wd, model_file=model_file)
         #: Reference to the Excel application. This attribute is `None` until
         #: model_init has been invoked.
@@ -91,11 +91,11 @@ def __init__(
         self.pointers = pointers
 
     @property
-    def workbook(self):
+    def workbook(self) -> str:
         return self.model_file
 
     @method_logger(__name__)
-    def model_init(self, policy):
+    def model_init(self, policy) -> None:
         """Method called to initialize the model.
 
         Parameters
@@ -130,7 +130,7 @@ def model_init(self, policy):
             _logger.debug(self.working_directory)
 
     @method_logger(__name__)
-    def run_experiment(self, experiment):
+    def run_experiment(self, experiment) -> dict:
         """Method for running an experiment.
 
         This implementation assumes that the names of the uncertainties correspond
@@ -174,7 +174,7 @@ def run_experiment(self, experiment):
         return results
 
     @method_logger(__name__)
-    def cleanup(self):
+    def cleanup(self) -> None:
         """Cleaning up prior to finishing performing experiments.
 
         This will close the workbook and close Excel
@@ -199,7 +199,7 @@ def cleanup(self):
         self.xl = None
         self.wb = None
 
-    def get_sheet(self, sheetname=None):
+    def get_sheet(self, sheetname: str | None = None):
         """Get a named worksheet, or the default worksheet if set.
 
         Parameters
@@ -227,7 +227,7 @@ def get_sheet(self, sheetname=None):
 
         return sheet
 
-    def get_wb_value(self, name):
+    def get_wb_value(self, name: str):
         """Extract a value from a cell of the excel workbook.
 
         Parameters
@@ -267,7 +267,7 @@ def get_wb_value(self, name):
 
         return value
 
-    def set_wb_value(self, name, value):
+    def set_wb_value(self, name: str, value) -> None:
         """Inject a value into a cell of the excel workbook.
 
         Parameters
@@ -304,7 +304,7 @@ def set_wb_value(self, name, value):
                 f"com error: no cell(s) named {this_range} found on sheet {this_sheet}"
             )
 
-    def get_wb_sheetnames(self):
+    def get_wb_sheetnames(self) -> list[str]:
         """Get the names of all the workbook's worksheets."""
         if self.wb:
             try:
diff --git a/ema_workbench/connectors/netlogo.py b/ema_workbench/connectors/netlogo.py
index 99682bec8..17707913a 100644
--- a/ema_workbench/connectors/netlogo.py
+++ b/ema_workbench/connectors/netlogo.py
@@ -123,7 +123,7 @@ def __init__(
         self.jvm_args = jvm_args
 
     @method_logger(__name__)
-    def model_init(self, policy: Sample):
+    def model_init(self, policy: Sample) -> None:
         """Method called to initialize the model.
 
         Parameters
@@ -148,7 +148,7 @@ def model_init(self, policy: Sample):
         _logger.debug("model opened")
 
     @method_logger(__name__)
-    def run_experiment(self, experiment: Experiment):
+    def run_experiment(self, experiment: Experiment) -> dict:
         """Method for running an experiment..
 
         Parameters
@@ -230,7 +230,7 @@ def run_experiment(self, experiment: Experiment):
 
         return results
 
-    def retrieve_output(self):
+    def retrieve_output(self) -> dict:
         """Method for retrieving output after a model run.
 
         Returns
@@ -241,7 +241,7 @@ def retrieve_output(self):
         return self.output
 
     @method_logger(__name__)
-    def cleanup(self):
+    def cleanup(self) -> None:
         """Cleanup after finishing all the experiments, but just prior to returning the results.
 
         This method gives a hook for doing any cleanup, such as closing applications.
@@ -259,7 +259,7 @@ def cleanup(self):
     #         jpype.shutdownJVM()
     #         self.netlogo = None
 
-    def _handle_outcomes(self, fns):
+    def _handle_outcomes(self, fns: dict[str, str]) -> dict:
         """Helper function for parsing outcomes."""
         results = {}
         for key, value in fns.items():
diff --git a/ema_workbench/connectors/pysd_connector.py b/ema_workbench/connectors/pysd_connector.py
index e5d4afe1a..480e6a7db 100644
--- a/ema_workbench/connectors/pysd_connector.py
+++ b/ema_workbench/connectors/pysd_connector.py
@@ -31,11 +31,11 @@ class BasePysdModel(AbstractModel):
     """
 
     @property
-    def mdl_file(self):
+    def mdl_file(self) -> str:
         return self._mdl_file
 
     @mdl_file.setter
-    def mdl_file(self, mdl_file):
+    def mdl_file(self, mdl_file: str) -> None:
         if not mdl_file.endswith(".mdl"):
             raise ValueError("model file needs to be a vensim .mdl file")
         if not os.path.isfile(mdl_file):
@@ -54,7 +54,7 @@ def __init__(self, name: str, mdl_file: str | None = None):
         self.model = None
 
     @method_logger(__name__)
-    def model_init(self, policy: Sample, **kwargs):
+    def model_init(self, policy: Sample, **kwargs) -> None:
         """Initialize the model."""
         super().model_init(policy)
 
@@ -63,7 +63,7 @@ def model_init(self, policy: Sample, **kwargs):
         self.model = pysd.read_vensim(self.mdl_file)
 
     @method_logger(__name__)
-    def run_experiment(self, experiment):
+    def run_experiment(self, experiment) -> dict:
         """Run the experiment."""
         res = self.model.run(params=experiment, return_columns=self.output_variables)
 
@@ -71,7 +71,7 @@ def run_experiment(self, experiment):
         output = {col: series.values for col, series in res.items()}
         return output
 
-    def reset_model(self):
+    def reset_model(self) -> None:
         """Method for resetting the model to its initial state.
 
         The default implementation only sets the outputs to an empty dict.
diff --git a/ema_workbench/connectors/vadere.py b/ema_workbench/connectors/vadere.py
index ef6ef0da2..0e3b6a38b 100644
--- a/ema_workbench/connectors/vadere.py
+++ b/ema_workbench/connectors/vadere.py
@@ -24,7 +24,7 @@
 ]
 
 
-def change_vadere_scenario(model_file, variable, value):
+def change_vadere_scenario(model_file: dict, variable: str, value: float) -> None:
     """Change variable in vadere .scenario file structure.
 
     Note that a vadere scenario takes the format of a nested directory.
@@ -47,7 +47,7 @@ def change_vadere_scenario(model_file, variable, value):
     reduce(operator.getitem, index[:-1], model_file)[index[-1]] = value
 
 
-def update_vadere_scenario(model_file, experiment, output_file):
+def update_vadere_scenario(model_file: str, experiment: dict, output_file: str) -> None:
     """Load a vadere .scenario file, change it depending on the passed experiment, and save it again as .scenario file.
 
     Parameters
@@ -83,7 +83,7 @@ class BaseVadereModel(FileModel):
 
     """
 
-    def __init__(self, name, vadere_jar, processor_files, wd, model_file):
+    def __init__(self, name: str, vadere_jar: str, processor_files: list[str], wd: str, model_file: str) -> None:
         """Init of class.
 
         Parameters
@@ -120,7 +120,7 @@ def __init__(self, name, vadere_jar, processor_files, wd, model_file):
         self.processor_files = processor_files
 
     @method_logger(__name__)
-    def model_init(self, policy):
+    def model_init(self, policy) -> None:
         """Method called to initialize the model.
 
         Parameters
@@ -133,7 +133,7 @@ def model_init(self, policy):
         super().model_init(policy)
 
     @method_logger(__name__)
-    def run_experiment(self, experiment):
+    def run_experiment(self, experiment) -> dict:
         """Run the experiment.
 
         Parameters
@@ -234,7 +234,7 @@ def run_experiment(self, experiment):
             pass
         return res
 
-    def cleanup(self):
+    def cleanup(self) -> None:
         """Cleanup after performing all experiments.
 
         This method gives a hook for doing any cleanup, such as closing applications.
diff --git a/ema_workbench/connectors/vensim.py b/ema_workbench/connectors/vensim.py
index 027a626fa..95d0175fc 100644
--- a/ema_workbench/connectors/vensim.py
+++ b/ema_workbench/connectors/vensim.py
@@ -36,7 +36,7 @@
 _logger = get_module_logger(__name__)
 
 
-def be_quiet():
+def be_quiet() -> None:
     """Turn off the work in progress dialog of Vensim.
 
     Defaults to 2, suppressing all windows, for more fine-grained control, use
@@ -45,7 +45,7 @@ def be_quiet():
     vensim_dll_wrapper.be_quiet(2)
 
 
-def load_model(file_name):
+def load_model(file_name: str) -> None:
     """Load the model.
 
     Parameters
@@ -68,7 +68,7 @@ def load_model(file_name):
         raise VensimError("vensim file not found") from w
 
 
-def read_cin_file(file_name):
+def read_cin_file(file_name: str) -> None:
     """Read a .cin file.
 
     Parameters
@@ -89,7 +89,7 @@ def read_cin_file(file_name):
         raise w
 
 
-def set_value(variable, value):
+def set_value(variable: str, value: int | float | list) -> None:
     """Set the value of a variable to value.
 
     current implementation only works for lookups and normal values. In case
@@ -117,7 +117,7 @@ def set_value(variable, value):
             _logger.warning("variable: '" + variable + "' not found")
 
 
-def run_simulation(file_name):
+def run_simulation(file_name: str) -> None:
     """Rn a model and store the results of the run in the specified .vdf file.
 
     The specified output file will be overwritten by default
@@ -145,7 +145,7 @@ def run_simulation(file_name):
         raise VensimError(str(w)) from w
 
 
-def get_data(filename, varname, step=1):
+def get_data(filename: str, varname: str, step: int = 1) -> list:
     """Retrieve data from simulation runs or imported data sets.
 
     Parameters
@@ -184,7 +184,7 @@ class VensimModel(SingleReplication, FileModel):
     """
 
     @property
-    def result_file(self):
+    def result_file(self) -> str:
         """Return path to results file."""
         return os.path.join(self.working_directory, self._result_file)
 
@@ -245,7 +245,7 @@ def __init__(
 
         _logger.debug("vensim interface init completed")
 
-    def model_init(self, policy: Sample):
+    def model_init(self, policy: Sample) -> None:
         """Init of the model.
 
         Parameters
@@ -290,7 +290,7 @@ def handle_underscores(variables: list[Variable]):
             raise EMAWarning(str(VensimWarning)) from w
 
     @method_logger(__name__)
-    def run_experiment(self, experiment: Experiment):
+    def run_experiment(self, experiment: Experiment) -> dict:
         """Run the experiment.
 
         The provided implementation assumes that the keys (i.e., the parameter names) in the
@@ -358,7 +358,7 @@ def check_data(result):
         return results
 
 
-def create_model_for_debugging(path_to_existing_model, path_to_new_model, error):
+def create_model_for_debugging(path_to_existing_model: str, path_to_new_model: str, error: str) -> None:
     """Create a vensim mdl file parameterized according to the experiment.
 
     To be able to debug the Vensim model, a few steps are needed:
diff --git a/ema_workbench/em_framework/callbacks.py b/ema_workbench/em_framework/callbacks.py
index 524300569..8399385a9 100644
--- a/ema_workbench/em_framework/callbacks.py
+++ b/ema_workbench/em_framework/callbacks.py
@@ -10,11 +10,14 @@
 
 """
 
+from __future__ import annotations
+
 import abc
 import csv
 import math
 import os
 import shutil
+from typing import TYPE_CHECKING
 
 import numpy as np
 import pandas as pd
@@ -27,9 +30,12 @@
     IntegerParameter,
     Parameter,
 )
-from .points import flatten_sample
+from .points import Experiment, flatten_sample
 from .util import ProgressTrackingMixIn
 
+if TYPE_CHECKING:
+    from typing import Any
+
 #
 # Created on 22 Jan 2013
 #
@@ -80,13 +86,13 @@ class AbstractCallback(ProgressTrackingMixIn, metaclass=abc.ABCMeta):
 
     def __init__(
         self,
-        uncertainties,
-        levers,
-        outcomes,
-        nr_experiments,
-        reporting_interval=None,
-        reporting_frequency=10,
-        log_progress=False,
+        uncertainties: list[Parameter],
+        levers: list[Parameter],
+        outcomes: list[Outcome],
+        nr_experiments: int,
+        reporting_interval: int | None = None,
+        reporting_frequency: int = 10,
+        log_progress: bool = False,
     ):
         """Init."""
         super().__init__(nr_experiments, reporting_frequency, _logger, log_progress)
@@ -102,7 +108,7 @@ def __init__(
         self.reporting_interval = reporting_interval
 
     @abc.abstractmethod
-    def __call__(self, experiment, outcomes):
+    def __call__(self, experiment: Experiment, outcomes: dict[str, Any]) -> None:
         """Method responsible for storing results.
 
         The implementation in this class only keeps track of how many runs
@@ -120,7 +126,7 @@ def __call__(self, experiment, outcomes):
         super().__call__(1)
 
     @abc.abstractmethod
-    def get_results(self):
+    def get_results(self) -> Any:
         """Method for retrieving the results.
 
         Called after all experiments have been completed. Any extension of AbstractCallback needs to
@@ -294,7 +300,7 @@ def _store_outcomes(self, case_id, outcomes):
                     )
                     self.results[outcome_name][case_id,] = outcome_res
 
-    def __call__(self, experiment, outcomes):
+    def __call__(self, experiment: Experiment, outcomes: dict[str, Any]) -> None:
         """Method responsible for storing results.
 
         This method calls :meth:`super` first, thus utilizing the logging provided there.
@@ -310,7 +316,7 @@ def __call__(self, experiment, outcomes):
         self._store_case(experiment)
         self._store_outcomes(experiment.experiment_id, outcomes)
 
-    def get_results(self):
+    def get_results(self) -> tuple[pd.DataFrame, dict[str, np.ndarray]]:
         """Return the experiments and their results."""
         results = {}
         for k, v in self.results.items():
@@ -376,12 +382,12 @@ class FileBasedCallback(AbstractCallback):
 
     def __init__(
         self,
-        uncertainties,
-        levers,
-        outcomes,
-        nr_experiments,
-        reporting_interval=100,
-        reporting_frequency=10,
+        uncertainties: list[Parameter],
+        levers: list[Parameter],
+        outcomes: list[Outcome],
+        nr_experiments: int,
+        reporting_interval: int = 100,
+        reporting_frequency: int = 10,
     ):
         """Init."""
         super().__init__(
diff --git a/ema_workbench/em_framework/evaluators.py b/ema_workbench/em_framework/evaluators.py
index add37f6ad..fb453f5b7 100644
--- a/ema_workbench/em_framework/evaluators.py
+++ b/ema_workbench/em_framework/evaluators.py
@@ -33,7 +33,7 @@
     LHSSampler,
     MonteCarloSampler,
 )
-from .util import determine_objects
+from .util import StdlibSeedLike, determine_objects
 
 # Created on 5 Mar 2017
 #
@@ -49,8 +49,6 @@
 
 _logger = get_module_logger(__name__)
 
-SeedLike = int | float | str | bytes | bytearray  # seedlike for stdlib random.seed
-
 
 class Samplers(enum.Enum):
     """Enum for different kinds of samplers."""
@@ -66,7 +64,7 @@ class Samplers(enum.Enum):
     MORRIS = MorrisSampler()
 
 
-SamplerTypes = Literal[
+type SamplerTypes = Literal[
     Samplers.MC,
     Samplers.LHS,
     Samplers.FF,
@@ -209,6 +207,44 @@ def perform_experiments(
             **kwargs,
         )
 
+    @overload
+    def optimize(
+            self,
+            algorithm: type[AbstractGeneticAlgorithm] = EpsNSGAII,
+            nfe: int = 10000,
+            searchover: str = "levers",
+            reference: Sample | None = None,
+            constraints: Iterable[Constraint] | None = None,
+            convergence_freq: int = 1000,
+            logging_freq: int = 5,
+            variator: type[Variator] | None = None,
+            rng: Iterable[StdlibSeedLike] | None = None,
+            initial_population: Iterable[Sample] | None = None,
+            filename: str | None = None,
+            directory: str | None = None,
+            **kwargs,
+    ) -> list[tuple[pd.DataFrame, pd.DataFrame]]:
+        ...
+
+    @overload
+    def optimize(
+            self,
+            algorithm: type[AbstractGeneticAlgorithm] = EpsNSGAII,
+            nfe: int = 10000,
+            searchover: str = "levers",
+            reference: Sample | None = None,
+            constraints: Iterable[Constraint] | None = None,
+            convergence_freq: int = 1000,
+            logging_freq: int = 5,
+            variator: type[Variator] | None = None,
+            rng: StdlibSeedLike | None = None,
+            initial_population: Iterable[Sample] | None = None,
+            filename: str | None = None,
+            directory: str | None = None,
+            **kwargs,
+    ) -> tuple[pd.DataFrame, pd.DataFrame]:
+        ...
+
     def optimize(
         self,
         algorithm: type[AbstractGeneticAlgorithm] = EpsNSGAII,
@@ -219,12 +255,12 @@ def optimize(
         convergence_freq: int = 1000,
         logging_freq: int = 5,
         variator: type[Variator] | None = None,
-        rng: SeedLike | Iterable[SeedLike] | None = None,
+        rng: StdlibSeedLike | Iterable[StdlibSeedLike] | None = None,
         initial_population: Iterable[Sample] | None = None,
         filename: str | None = None,
         directory: str | None = None,
         **kwargs,
-    ) -> tuple[pd.DataFrame, pd.DataFrame]:
+    ):
         """Convenience method for outcome optimization.
 
         A call to this method is forwarded to :func:optimize, with evaluator and models
@@ -263,7 +299,7 @@ def robust_optimize(
         nfe: int = 10000,
         convergence_freq: int = 1000,
         logging_freq: int = 5,
-        rng: SeedLike | Iterable[SeedLike] | None = None,
+        rng: StdlibSeedLike | Iterable[StdlibSeedLike] | None = None,
         **kwargs,
     ) -> tuple[pd.DataFrame, pd.DataFrame]:
         """Convenience method for robust optimization.
@@ -578,7 +614,7 @@ def optimize(
     convergence_freq: int = 1000,
     logging_freq: int = 5,
     variator: Variator = None,
-    rng: Iterable[SeedLike] | None = None,
+    rng: Iterable[StdlibSeedLike] | None = None,
     initial_population: Iterable[Sample] | None = None,
     filename: str | None = None,
     directory: str | None = None,
@@ -598,7 +634,7 @@ def optimize(
     convergence_freq: int = 1000,
     logging_freq: int = 5,
     variator: Variator = None,
-    rng: SeedLike | None = None,
+    rng: StdlibSeedLike | None = None,
     initial_population: Iterable[Sample] | None = None,
     filename: str | None = None,
     directory: str | None = None,
@@ -617,7 +653,7 @@ def optimize(
     convergence_freq: int = 1000,
     logging_freq: int = 5,
     variator: Variator = None,
-    rng: SeedLike | Iterable[SeedLike] | None = None,
+    rng: StdlibSeedLike | Iterable[StdlibSeedLike] | None = None,
     initial_population: Iterable[Sample] | None = None,
     filename: str | None = None,
     directory: str | None = None,
@@ -731,7 +767,7 @@ def robust_optimize(
     convergence_freq: int = 1000,
     logging_freq: int = 5,
     variator: Variator = None,
-    rng: SeedLike | None = None,
+    rng: StdlibSeedLike | None = None,
     initial_population: Iterable[Sample] | None = None,
     filename: str | None = None,
     directory: str | None = None,
@@ -751,7 +787,7 @@ def robust_optimize(
     convergence_freq: int = 1000,
     logging_freq: int = 5,
     variator: Variator = None,
-    rng: Iterable[SeedLike] | None = None,
+    rng: Iterable[StdlibSeedLike] | None = None,
     initial_population: Iterable[Sample] | None = None,
     filename: str | None = None,
     directory: str | None = None,
@@ -770,7 +806,7 @@ def robust_optimize(
     convergence_freq: int = 1000,
     logging_freq: int = 5,
     variator: Variator = None,
-    rng: SeedLike | Iterable[SeedLike] | None = None,
+    rng: StdlibSeedLike | Iterable[StdlibSeedLike] | None = None,
     initial_population: Iterable[Sample] | None = None,
     filename: str | None = None,
     directory: str | None = None,
diff --git a/ema_workbench/em_framework/futures_util.py b/ema_workbench/em_framework/futures_util.py
index 41d8fbfb8..fe75d3777 100644
--- a/ema_workbench/em_framework/futures_util.py
+++ b/ema_workbench/em_framework/futures_util.py
@@ -1,20 +1,27 @@
 """Utilities for futures modules."""
 
+from __future__ import annotations
+
 import os
 import random
 import shutil
 import string
 import time
 from collections import defaultdict
+from collections.abc import Collection
+from typing import TYPE_CHECKING
 
 from ..util import get_module_logger
 
+if TYPE_CHECKING:
+    from .model import AbstractModel
+
 __all__ = ["determine_rootdir", "finalizer", "setup_working_directories"]
 
 _logger = get_module_logger(__name__)
 
 
-def determine_rootdir(msis):
+def determine_rootdir(msis: Collection[AbstractModel]) -> str | None:
     """Determine common root directory for all models."""
     for model in msis:
         try:
@@ -32,10 +39,10 @@ def determine_rootdir(msis):
     return root_dir
 
 
-def finalizer(experiment_runner):
+def finalizer(experiment_runner: AbstractModel) -> callable:
     """Cleanup."""
 
-    def finalizer(tmpdir):
+    def finalizer(tmpdir: str | None) -> None:
         _logger.info("finalizing")
 
         experiment_runner.cleanup()
@@ -52,7 +59,7 @@ def finalizer(tmpdir):
     return finalizer
 
 
-def setup_working_directories(models, root_dir):
+def setup_working_directories(models: Collection[AbstractModel], root_dir: str) -> str | None:
     """Setup working directories when running in parallel.
 
     Copies the working directory of each model to a process specific
diff --git a/ema_workbench/em_framework/model.py b/ema_workbench/em_framework/model.py
index bdf252b54..1f057c21b 100644
--- a/ema_workbench/em_framework/model.py
+++ b/ema_workbench/em_framework/model.py
@@ -64,7 +64,7 @@ class AbstractModel(NamedObject):
     """
 
     @property
-    def outcomes_output(self):
+    def outcomes_output(self) -> dict:
         """Getter for outcomes output."""
         return self._outcomes_output
 
@@ -76,7 +76,7 @@ def outcomes_output(self, outputs):
             self._outcomes_output[outcome.name] = outcome.process(data)
 
     @property
-    def output_variables(self):
+    def output_variables(self) -> list[str]:
         """Getter for output variables."""
         if self._output_variables is None:
             self._output_variables = [
@@ -177,7 +177,7 @@ def _transform(self, sampled_parameters: Sample, parameters: list[Variable]):
         sampled_parameters.data = temp
 
     @method_logger(__name__)
-    def run_model(self, scenario: Sample, policy: Sample, constants: Sample):
+    def run_model(self, scenario: Sample, policy: Sample, constants: Sample) -> None:
         """Run the model for the specified scenario, policy, and constants.
 
         Parameters
@@ -195,7 +195,7 @@ def run_model(self, scenario: Sample, policy: Sample, constants: Sample):
         self._transform(constants, self.constants)
 
     @method_logger(__name__)
-    def initialized(self, policy: Sample):
+    def initialized(self, policy: Sample) -> bool:
         """Check if model has been initialized.
 
         Parameters
@@ -297,7 +297,7 @@ def replications(self, replications: int | list[dict]):
             )
 
     @method_logger(__name__)
-    def run_model(self, scenario: Sample, policy: Sample, constants: Sample):
+    def run_model(self, scenario: Sample, policy: Sample, constants: Sample) -> None:
         """Run the model for the specified scenario, policy, and constants.
 
         Parameters
@@ -333,7 +333,7 @@ class SingleReplication(AbstractModel):
     """Base class for models that require only a single replication."""
 
     @method_logger(__name__)
-    def run_model(self, scenario: Sample, policy: Sample, constants: Sample):
+    def run_model(self, scenario: Sample, policy: Sample, constants: Sample) -> None:
         """Run the model for the specified scenario, policy, and constants.
 
         Parameters
diff --git a/ema_workbench/em_framework/optimization.py b/ema_workbench/em_framework/optimization.py
index 05e173313..f14b24bf3 100644
--- a/ema_workbench/em_framework/optimization.py
+++ b/ema_workbench/em_framework/optimization.py
@@ -48,7 +48,7 @@
     RealParameter,
 )
 from .points import Sample, SampleCollection
-from .util import ProgressTrackingMixIn
+from .util import ProgressTrackingMixIn, StdlibSeedLike
 
 # Created on 5 Jun 2017
 #
@@ -64,9 +64,6 @@
 _logger = get_module_logger(__name__)
 
 
-SeedLike = int | float | str | bytes | bytearray  # seedlike for stdlib random.seed
-
-
 class Problem(PlatypusProblem):
     """Small extension to Platypus problem object.
 
@@ -165,7 +162,7 @@ def to_platypus_types(decision_variables: Iterable[Parameter]) -> list[platypus.
 
 def to_dataframe(
     solutions: Iterable[platypus.Solution], dvnames: list[str], outcome_names: list[str]
-):
+) -> pd.DataFrame:
     """Helper function to turn a collection of platypus Solution instances into a pandas DataFrame.
 
     Parameters
@@ -193,7 +190,7 @@ def to_dataframe(
     return results
 
 
-def process_jobs(jobs: list[platypus.core.EvaluateSolution]):
+def process_jobs(jobs: list[platypus.core.EvaluateSolution]) -> tuple[Sample | SampleCollection | int, list[Sample] | SampleCollection]:
     """Helper function to map jobs generated by platypus to Sample instances.
 
     Parameters
@@ -233,7 +230,7 @@ def evaluate(
     experiments: pd.DataFrame,
     outcomes: dict[str, np.ndarray],
     problem: Problem,
-):
+) -> None:
     """Helper function for mapping the results from perform_experiments back to what platypus needs."""
     searchover = problem.searchover
     outcome_names = problem.outcome_names
@@ -479,7 +476,7 @@ def epsilon_nondominated(
     return to_dataframe(archive, problem.parameter_names, problem.outcome_names)
 
 
-def rebuild_platypus_population(archive: pd.DataFrame, problem: Problem):
+def rebuild_platypus_population(archive: pd.DataFrame, problem: Problem) -> list[Solution]:
     """Rebuild a population of platypus Solution instances.
 
     Parameters
@@ -689,9 +686,9 @@ def _optimize(
     initial_population: Iterable[Sample] | None = None,
     filename: str | None = None,
     directory: str | None = None,
-    rng: None | SeedLike = None,
+    rng: None | StdlibSeedLike = None,
     **kwargs,
-):
+) -> tuple[pd.DataFrame, pd.DataFrame]:
     """Helper function for optimization."""
     klass = problem.types[0].__class__
 
diff --git a/ema_workbench/em_framework/outcomes.py b/ema_workbench/em_framework/outcomes.py
index e6f273185..b3a205785 100644
--- a/ema_workbench/em_framework/outcomes.py
+++ b/ema_workbench/em_framework/outcomes.py
@@ -3,9 +3,11 @@
 import abc
 import collections
 import numbers
+import tarfile
 import warnings
-from collections.abc import Callable
+from collections.abc import Callable, Sequence
 from io import BytesIO
+from typing import Any
 
 import numpy as np
 import pandas as pd
@@ -56,7 +58,7 @@ def __call__(self, outcome):
         else:
             pass  # multiple instances of the same class and name is fine
 
-    def serialize(self, name, values):
+    def serialize(self, name: str, values: np.ndarray | pd.DataFrame) -> tuple[BytesIO, str]:
         """Serialize the given outcome.
 
         Parameters
@@ -77,7 +79,7 @@ def serialize(self, name, values):
 
         return stream, f"{name}.{extension}"
 
-    def deserialize(self, name, filename, archive):
+    def deserialize(self, name: str, filename: str, archive: tarfile.TarFile) -> np.ndarray:
         """Serialize the given outcome."""
         return self.outcomes[name].from_disk(filename, archive)
 
@@ -127,13 +129,13 @@ class Outcome(Variable, metaclass=abc.ABCMeta):
 
     def __init__(
         self,
-        name,
-        kind=INFO,
-        variable_name=None,
-        function=None,
-        shape=None,
-        dtype=None,
-    ):
+        name: str,
+        kind: int = INFO,
+        variable_name: str | Sequence[str] | None = None,
+        function: Callable[..., Any] | None = None,
+        shape: tuple[int, ...] | None = None,
+        dtype: np.dtype | type | None = None,
+    ) -> None:
         """Init."""
         super().__init__(name)
 
@@ -165,7 +167,7 @@ def __init__(
         self.shape = shape
         self.dtype = dtype
 
-    def process(self, values):
+    def process(self, values: list[Any]) -> Any:
         """Process the values."""
         if self.function:
             var_names = self.variable_name
@@ -226,7 +228,7 @@ def __hash__(self):  # noqa: D105
 
     @classmethod
     @abc.abstractmethod
-    def to_disk(cls, values):
+    def to_disk(cls, values: np.ndarray | pd.DataFrame) -> tuple[BytesIO, str]:
         """Helper function for writing outcome to disk.
 
         Parameters
@@ -243,7 +245,7 @@ def to_disk(cls, values):
 
     @classmethod
     @abc.abstractmethod
-    def from_disk(cls, filename, archive):
+    def from_disk(cls, filename: str, archive: tarfile.TarFile) -> np.ndarray:
         """Helper function for loading from disk.
 
         Parameters
@@ -294,12 +296,12 @@ class ScalarOutcome(Outcome):
 
     def __init__(
         self,
-        name,
-        kind=Outcome.INFO,
-        variable_name=None,
-        function=None,
-        dtype=None,
-    ):
+        name: str,
+        kind: int = Outcome.INFO,
+        variable_name: str | Sequence[str] | None = None,
+        function: Callable[..., Any] | None = None,
+        dtype: np.dtype | type | None = None,
+    ) -> None:
         """Init."""
         shape = None
         if dtype is not None:
diff --git a/ema_workbench/em_framework/outputspace_exploration.py b/ema_workbench/em_framework/outputspace_exploration.py
index dc074b46f..6ae64eed2 100644
--- a/ema_workbench/em_framework/outputspace_exploration.py
+++ b/ema_workbench/em_framework/outputspace_exploration.py
@@ -15,6 +15,8 @@
 
 """
 
+from __future__ import annotations
+
 import functools
 import math
 
@@ -34,6 +36,7 @@
     Multimethod,
     PlatypusConfig,
     RandomGenerator,
+    Solution,
     TournamentSelector,
 )
 
@@ -54,11 +57,11 @@ class Novelty(Dominance):
 
     """
 
-    def __init__(self, algorithm):
+    def __init__(self, algorithm: OutputSpaceExplorationAlgorithm) -> None:
         super().__init__()
         self.algorithm = algorithm
 
-    def compare(self, winner, candidate):
+    def compare(self, winner: Solution, candidate: Solution) -> int:
         """Compare two solutions.
 
         Returns -1 if the first solution dominates the second, 1 if the
@@ -101,7 +104,7 @@ class HitBox(Archive):
 
     """
 
-    def __init__(self, grid_spec):
+    def __init__(self, grid_spec: list[tuple[float, float, float]]) -> None:
         """Init."""
         super().__init__(None)
         self.archive = {}
@@ -111,7 +114,7 @@ def __init__(self, grid_spec):
         self.improvements = 0
         self.overall_novelty = 0
 
-    def add(self, solution):
+    def add(self, solution: Solution) -> bool:
         """Add a solution to the archive."""
         key = get_index_for_solution(solution, self.grid_spec)
 
@@ -146,7 +149,7 @@ def add(self, solution):
 
         return True
 
-    def get_novelty_score(self, solution):
+    def get_novelty_score(self, solution: Solution) -> float:
         """Return the novelty score of the solution."""
         key = get_index_for_solution(solution, self.grid_spec)
         return 1 / self.grid_counter[key]
@@ -212,7 +215,7 @@ def __init__(
         self.comparator = Novelty(self)
         self.add_extension(AdaptiveTimeContinuationExtension())
 
-    def step(self):
+    def step(self) -> None:
         """A single step of the algorithm."""
         if self.nfe == 0:
             self.initialize()
@@ -221,7 +224,7 @@ def step(self):
 
         self.result = self.archive
 
-    def initialize(self):
+    def initialize(self) -> None:
         """Initialize the algorithm."""
         super().initialize()
 
@@ -231,7 +234,7 @@ def initialize(self):
         if self.variator is None:
             self.variator = PlatypusConfig.default_variator(self.problem)
 
-    def iterate(self):
+    def iterate(self) -> None:
         """A signle iteration of the algorithm."""
         offspring = []
 
@@ -249,7 +252,7 @@ def iterate(self):
         self.population = offspring[: self.population_size]
 
 
-def get_index_for_solution(solution, grid_spec):
+def get_index_for_solution(solution: Solution, grid_spec: list[tuple[float, float, float]]) -> tuple[int, ...]:
     """Maps the objectives to the key for the grid cell into which this solution falls.
 
     Parameters
@@ -271,7 +274,7 @@ def get_index_for_solution(solution, grid_spec):
     return key
 
 
-def get_bin_index(value, minumum_value, epsilon):
+def get_bin_index(value: float, minumum_value: float, epsilon: float) -> int:
     """Maps the value for a single objective to the index of the grid cell along that dimension.
 
     Parameters
diff --git a/ema_workbench/em_framework/parameters.py b/ema_workbench/em_framework/parameters.py
index d629b53ac..3528a0f23 100644
--- a/ema_workbench/em_framework/parameters.py
+++ b/ema_workbench/em_framework/parameters.py
@@ -98,7 +98,7 @@ def __init__(self, name: str, value: Any):
         self.value = value
 
 
-def create_category(cat):
+def create_category(cat: Any) -> Category:
     """Helper function for creating a Category object."""
     if isinstance(cat, Category):
         return cat
@@ -133,7 +133,7 @@ class Parameter(Variable, metaclass=abc.ABCMeta):
     default = None
 
     @property
-    def resolution(self):
+    def resolution(self) -> list | None:
         """Getter for resolution."""
         return self._resolution
 
@@ -168,7 +168,7 @@ def __init__(
         self.uniform = True
 
     @classmethod
-    def from_dist(cls, name: str, dist, **kwargs):
+    def from_dist(cls, name: str, dist: sp.stats.rv_continuous | sp.stats.rv_discrete, **kwargs: Any) -> "Parameter":
         """Factory method for creating a Parameter from a scipy distribution.
 
         Alternative constructor for creating a parameter from a frozen
@@ -444,7 +444,7 @@ def __init__(
         self.resolution = list(range(len(self.categories)))
         self.multivalue = multivalue
 
-    def index_for_cat(self, category):
+    def index_for_cat(self, category: str) -> int:
         """Return index of category.
 
         Parameters
@@ -462,7 +462,7 @@ def index_for_cat(self, category):
                 return i
         raise ValueError(f"Category {category} not found")
 
-    def cat_for_index(self, index: int):
+    def cat_for_index(self, index: int) -> Category:
         """Return category given index.
 
         Parameters
@@ -699,7 +699,7 @@ def latent_parameters(self) -> list[Parameter]:
                     parameters.append(latent_parameter)
         return parameters
 
-    def copy(self):
+    def copy(self) -> "ParameterMap":
         copy = self.__class__()
         copy._data = self._data.copy()
 
diff --git a/ema_workbench/em_framework/points.py b/ema_workbench/em_framework/points.py
index c1d042fd0..b53d1c4e2 100644
--- a/ema_workbench/em_framework/points.py
+++ b/ema_workbench/em_framework/points.py
@@ -7,7 +7,7 @@
 
 import itertools
 import math
-from collections.abc import Generator, Iterable, Sequence
+from collections.abc import Generator, Iterable
 from typing import TYPE_CHECKING, Literal, overload
 
 import numpy as np
@@ -21,7 +21,7 @@
     Parameter,
     ParameterMap,
 )
-from .util import Counter, NamedDict, NamedObject, combine
+from .util import Counter, NamedDict, NamedObject, NumpySeedLike, RNGLike, combine
 
 if TYPE_CHECKING:
     from .optimization import Problem
@@ -37,9 +37,6 @@
 ]
 _logger = get_module_logger(__name__)
 
-SeedLike = int | np.integer | Sequence[int] | np.random.SeedSequence
-RNGLike = np.random.Generator | np.random.BitGenerator
-
 
 class Sample(NamedDict):
     """A point in parameter space."""
@@ -233,7 +230,7 @@ def combine(
         self,
         other: SampleCollection,
         how: Literal["full_factorial", "sample", "cycle"],
-        rng: SeedLike | RNGLike | None = None,
+        rng: NumpySeedLike | RNGLike | None = None,
     ) -> SampleCollection:
         """Combine two SampleCollections into a new SampleCollection.
 
@@ -384,7 +381,7 @@ def experiment_generator(
     scenarios: Iterable[Sample],
     policies: Iterable[Sample],
     combine: Literal["full_factorial", "sample", "cycle"] = "full_factorial",
-    rng: SeedLike | RNGLike | None = None,
+    rng: NumpySeedLike | RNGLike | None = None,
 ) -> Generator[Experiment]:
     """Generator function which yields experiments.
 
diff --git a/ema_workbench/em_framework/samplers.py b/ema_workbench/em_framework/samplers.py
index 68f3968fd..a3c5bfae3 100644
--- a/ema_workbench/em_framework/samplers.py
+++ b/ema_workbench/em_framework/samplers.py
@@ -8,7 +8,7 @@
 
 import abc
 import itertools
-from collections.abc import Iterable, Sequence
+from collections.abc import Iterable
 
 import numpy as np
 import scipy.stats as stats
@@ -20,6 +20,7 @@
     ParameterMap,
 )
 from ema_workbench.em_framework.points import SampleCollection
+from ema_workbench.em_framework.util import NumpySeedLike, RNGLike
 
 # Created on 16 aug. 2011
 #
@@ -33,10 +34,6 @@
 ]
 
 
-SeedLike = int | np.integer | Sequence[int] | np.random.SeedSequence
-RNGLike = np.random.Generator | np.random.BitGenerator
-
-
 class AbstractSampler(metaclass=abc.ABCMeta):
     """Abstract base class from which different samplers can be derived.
 
@@ -51,7 +48,7 @@ def generate_samples(
         self,
         parameters: ParameterMap | Iterable[Parameter],
         size: int,
-        rng: SeedLike | RNGLike | None = None,
+        rng: NumpySeedLike | RNGLike | None = None,
         **kwargs,
     ) -> "SampleCollection":
         """Generate n samples from the parameters.
@@ -105,7 +102,7 @@ def generate_samples(
         self,
         parameters: ParameterMap,
         size: int,
-        rng: SeedLike | RNGLike | None = None,
+        rng: NumpySeedLike | RNGLike | None = None,
         **kwargs,
     ) -> "SampleCollection":
         """Generate samples using latin hypercube sampling.
@@ -148,7 +145,7 @@ def generate_samples(
         self,
         parameters: ParameterMap | Iterable[Parameter],
         size: int,
-        rng: SeedLike | RNGLike | None = None,
+        rng: NumpySeedLike | RNGLike | None = None,
         **kwargs,
     ) -> "SampleCollection":
         """Generate samples using Monte Carlo sampling.
@@ -189,7 +186,7 @@ def generate_samples(
         self,
         parameters: ParameterMap | Iterable[Parameter],
         size: int,
-        rng: SeedLike | RNGLike | None = None,
+        rng: NumpySeedLike | RNGLike | None = None,
         **kwargs,
     ) -> "SampleCollection":
         """Generate samples using full factorial sampling.
diff --git a/ema_workbench/em_framework/util.py b/ema_workbench/em_framework/util.py
index 951e319f8..470583d8f 100644
--- a/ema_workbench/em_framework/util.py
+++ b/ema_workbench/em_framework/util.py
@@ -6,29 +6,41 @@
 import copy
 import itertools
 import warnings
-from collections.abc import Iterable, Iterator, KeysView, Mapping, MutableMapping
-from typing import Generic, Literal, TypeVar
-
+from collections.abc import (
+    Iterable,
+    Iterator,
+    KeysView,
+    Mapping,
+    MutableMapping,
+    Sequence,
+)
+from typing import Literal
+
+import numpy as np
 import tqdm
 
 from ..util import EMAError
 
+type NumpySeedLike = int | np.integer | Sequence[int] | np.random.SeedSequence
+type RNGLike = np.random.Generator | np.random.BitGenerator
+type StdlibSeedLike = int | float | str | bytes | bytearray
+
 __all__ = [
     "Counter",
     "NamedDict",
     "NamedObject",
     "NamedObjectMap",
     "NamedObjectMapDescriptor",
+    "NumpySeedLike",
     "ProgressTrackingMixIn",
+    "RNGLike",
+    "StdlibSeedLike",
     "Variable",
     "combine",
     "determine_objects",
     "representation",
 ]
 
-T = TypeVar("T")
-
-
 class NamedObject:
     """Base object with a name attribute."""
 
@@ -90,7 +102,7 @@ def __init__(self, name: str, variable_name: str | list[str] | None = None):
         self.variable_name = variable_name
 
 
-class NamedObjectMap(MutableMapping, Generic[T]):
+class NamedObjectMap[T](MutableMapping):
     """A named object mapping class."""
 
     def __init__(self, kind: type[T]) -> None:
@@ -228,7 +240,7 @@ def copy(self):
         """Return a shallow copy of this object."""
         return copy.copy(self)
 
-    def __getitem__(self, key) -> T:  # noqa: D105
+    def __getitem__(self, key):  # noqa: D105
         return self.data[key]
 
     def __setitem__(self, key, value):  # noqa: D105
@@ -237,7 +249,7 @@ def __setitem__(self, key, value):  # noqa: D105
     def __delitem__(self, key):  # noqa: D105
         del self.data[key]
 
-    def __iter__(self) -> Iterator[T]:  # noqa: D105
+    def __iter__(self) -> Iterator:  # noqa: D105
         return iter(self.data)
 
     def __len__(self) -> int:  # noqa: D105
diff --git a/ema_workbench/util/ema_logging.py b/ema_workbench/util/ema_logging.py
index 5c164a45d..293f4f280 100644
--- a/ema_workbench/util/ema_logging.py
+++ b/ema_workbench/util/ema_logging.py
@@ -44,7 +44,7 @@ def create_module_logger(name: str | None = None) -> logging.Logger:
     return logger
 
 
-def get_module_logger(name) -> logging.Logger:
+def get_module_logger(name: str) -> logging.Logger:
     """Return a module logger with the given name."""
     try:
         logger = _module_loggers[name]
@@ -64,7 +64,7 @@ def get_module_logger(name) -> logging.Logger:
 class TemporaryFilter(logging.Filter):
     """Helper class to temporarily log messages."""
 
-    def __init__(self, *args, level: int = 0, func_name=None, **kwargs):
+    def __init__(self, *args, level: int = 0, func_name: str | None = None, **kwargs):
         super().__init__(*args, **kwargs)
         self.level = level
         self.func_name = func_name
@@ -141,7 +141,7 @@ def temporary_filter(
         v.removeFilter(k)
 
 
-def method_logger(name):
+def method_logger(name: str) -> callable:
     """Wrap method so that every call to it is logged."""
     logger = get_module_logger(name)
     classname = inspect.getouterframes(inspect.currentframe())[1][3]
@@ -180,7 +180,7 @@ def get_rootlogger() -> logging.Logger:
     return _rootlogger
 
 
-def log_to_stderr(level=None, pass_root_logger_level=False):
+def log_to_stderr(level: int | None = None, pass_root_logger_level: bool = False) -> logging.Logger:
     """Turn on logging and add a handler which prints to stderr.
 
     Parameters
diff --git a/ema_workbench/util/utilities.py b/ema_workbench/util/utilities.py
index 9eb04a179..90cbdb3d4 100644
--- a/ema_workbench/util/utilities.py
+++ b/ema_workbench/util/utilities.py
@@ -4,6 +4,7 @@
 import json
 import os
 import tarfile
+from collections.abc import Callable
 from io import BytesIO
 
 import numpy as np
@@ -19,7 +20,7 @@
 _logger = get_module_logger(__name__)
 
 
-def load_results(file_name):
+def load_results(file_name: str) -> tuple[pd.DataFrame, dict[str, np.ndarray]]:
     """Load the specified tar.gz file.
 
     the file is assumed to be saves using save_results.
@@ -170,7 +171,7 @@ def load_results_old(archive):
     return experiments, outcomes_new
 
 
-def save_results(results, file_name):
+def save_results(results: tuple[pd.DataFrame, dict[str, np.ndarray]], file_name: str) -> None:
     """Save the results to the specified tar.gz file.
 
     The way in which results are stored depends. Experiments are saved
@@ -231,7 +232,10 @@ def add_file(tararchive, stream, filename):
     _logger.info(f"results saved successfully to {file_name}")
 
 
-def merge_results(results1, results2):
+def merge_results(
+    results1: tuple[pd.DataFrame, dict[str, np.ndarray]],
+    results2: tuple[pd.DataFrame, dict[str, np.ndarray]],
+) -> tuple[pd.DataFrame, dict[str, np.ndarray]]:
     """Convenience function for merging results from the workbench.
 
     The function merges results2 with results1. For the experiments,
@@ -285,7 +289,7 @@ def merge_results(results1, results2):
     return mr
 
 
-def get_ema_project_home_dir():
+def get_ema_project_home_dir() -> str:
     try:
         config_file_name = "expworkbench.cfg"
         directory = os.path.dirname(__file__)
@@ -305,7 +309,10 @@ def get_ema_project_home_dir():
         return os.getcwd()
 
 
-def process_replications(data, aggregation_func=np.mean):
+def process_replications(
+    data: dict[str, np.ndarray] | tuple[pd.DataFrame, dict[str, np.ndarray]],
+    aggregation_func: Callable[..., np.ndarray] = np.mean,
+) -> dict[str, np.ndarray] | tuple[pd.DataFrame, dict[str, np.ndarray]]:
     """Convenience function for processing the replications of a stochastic model outcomes.
 
     The default behavior is to take the mean of the replications. This reduces
diff --git a/pyproject.toml b/pyproject.toml
index 108d4a7b2..8d2decdc6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -74,7 +74,7 @@ exclude = [
     "build",
 ]
 
-target-version = "py311"
+target-version = "py312"
 
 [tool.ruff.lint]
 select = [