diff --git a/OMPython/ModelicaSystem.py b/OMPython/ModelicaSystem.py
index e9c247b3..d41782b6 100644
--- a/OMPython/ModelicaSystem.py
+++ b/OMPython/ModelicaSystem.py
@@ -32,20 +32,24 @@
  CONDITIONS OF OSMC-PL.
 """
 
+import ast
 import csv
 from dataclasses import dataclass
 import importlib
+import itertools
 import logging
 import numbers
 import numpy as np
 import os
 import pathlib
 import platform
+import queue
 import re
 import subprocess
 import tempfile
 import textwrap
-from typing import Optional, Any
+import threading
+from typing import Any, Optional
 import warnings
 import xml.etree.ElementTree as ET
 
@@ -133,7 +137,7 @@ def __init__(self, runpath: pathlib.Path, modelname: str, timeout: Optional[int]
         self._args: dict[str, str | None] = {}
         self._arg_override: dict[str, str] = {}
 
-    def arg_set(self, key: str, val: Optional[str | dict] = None) -> None:
+    def arg_set(self, key: str, val: Optional[str | dict[str, Any]] = None) -> None:
         """
         Set one argument for the executable model.
 
@@ -176,7 +180,7 @@ def arg_get(self, key: str) -> Optional[str | dict]:
 
         return None
 
-    def args_set(self, args: dict[str, Optional[str | dict[str, str]]]) -> None:
+    def args_set(self, args: dict[str, Optional[str | dict[str, Any]]]) -> None:
         """
         Define arguments for the model executable.
 
@@ -419,7 +423,6 @@ def __init__(
         self.inputFlag = False  # for model with input quantity
         self.simulationFlag = False  # if the model is simulated?
         self.outputFlag = False
-        self.csvFile: Optional[pathlib.Path] = None  # for storing inputs condition
         self.resultfile: Optional[pathlib.Path] = None  # for storing result file
         self.variableFilter = variableFilter
 
@@ -867,29 +870,39 @@ def getOptimizationOptions(self, names=None):  # 10
 
         raise ModelicaSystemError("Unhandled input for getOptimizationOptions()")
 
-    def simulate(self, resultfile: Optional[str] = None, simflags: Optional[str] = None,
-                 simargs: Optional[dict[str, Optional[str | dict[str, str]]]] = None,
-                 timeout: Optional[int] = None):  # 11
+    def simulate_cmd(
+            self,
+            resultfile: pathlib.Path,
+            simflags: Optional[str] = None,
+            simargs: Optional[dict[str, Optional[str | dict[str, str]]]] = None,
+            timeout: Optional[int] = None,
+    ) -> ModelicaSystemCmd:
         """
-        This method simulates model according to the simulation options.
-        usage
-        >>> simulate()
-        >>> simulate(resultfile="a.mat")
-        >>> simulate(simflags="-noEventEmit -noRestart -override=e=0.3,g=10")  # set runtime simulation flags
-        >>> simulate(simargs={"noEventEmit": None, "noRestart": None, "override": "e=0.3,g=10"})  # using simargs
+        This method prepares the simulates model according to the simulation options. It returns an instance of
+        ModelicaSystemCmd which can be used to run the simulation.
+
+        Due to the tempdir being unique for the ModelicaSystem instance, *NEVER* use this to create several simulations
+        with the same instance of ModelicaSystem! Restart each simulation process with a new instance of ModelicaSystem.
+
+        However, if only non-structural parameters are used, it is possible to reuse an existing instance of
+        ModelicaSystem to create several version ModelicaSystemCmd to run the model using different settings.
+
+        Parameters
+        ----------
+        resultfile
+        simflags
+        simargs
+        timeout
+
+        Returns
+        -------
+            An instance if ModelicaSystemCmd to run the requested simulation.
         """
 
         om_cmd = ModelicaSystemCmd(runpath=self.tempdir, modelname=self.modelName, timeout=timeout)
 
-        if resultfile is None:
-            # default result file generated by OM
-            self.resultfile = self.tempdir / f"{self.modelName}_res.mat"
-        elif os.path.exists(resultfile):
-            self.resultfile = pathlib.Path(resultfile)
-        else:
-            self.resultfile = self.tempdir / resultfile
-        # always define the resultfile to use
-        om_cmd.arg_set(key="r", val=self.resultfile.as_posix())
+        # always define the result file to use
+        om_cmd.arg_set(key="r", val=resultfile.as_posix())
 
         # allow runtime simulation flags from user input
         if simflags is not None:
@@ -910,6 +923,9 @@ def simulate(self, resultfile: Optional[str] = None, simflags: Optional[str] = N
             om_cmd.arg_set(key="overrideFile", val=overrideFile.as_posix())
 
         if self.inputFlag:  # if model has input quantities
+            # csvfile is based on name used for result file
+            csvfile = resultfile.parent / f"{resultfile.stem}.csv"
+
             for i in self.inputlist:
                 val = self.inputlist[i]
                 if val is None:
@@ -921,9 +937,35 @@ def simulate(self, resultfile: Optional[str] = None, simflags: Optional[str] = N
                     raise ModelicaSystemError(f"startTime not matched for Input {i}!")
                 if float(self.simulateOptions["stopTime"]) != val[-1][0]:
                     raise ModelicaSystemError(f"stopTime not matched for Input {i}!")
-            self.csvFile = self.createCSVData()  # create csv file
 
-            om_cmd.arg_set(key="csvInput", val=self.csvFile.as_posix())
+            # write csv file and store the name
+            csvfile = self.createCSVData(csvfile=csvfile)
+
+            om_cmd.arg_set(key="csvInput", val=csvfile.as_posix())
+
+        return om_cmd
+
+    def simulate(self, resultfile: Optional[str] = None, simflags: Optional[str] = None,
+                 simargs: Optional[dict[str, Optional[str | dict[str, str]]]] = None,
+                 timeout: Optional[int] = None):  # 11
+        """
+        This method simulates model according to the simulation options.
+        usage
+        >>> simulate()
+        >>> simulate(resultfile="a.mat")
+        >>> simulate(simflags="-noEventEmit -noRestart -override=e=0.3,g=10")  # set runtime simulation flags
+        >>> simulate(simargs={"noEventEmit": None, "noRestart": None, "override": "e=0.3,g=10"})  # using simargs
+        """
+
+        if resultfile is None:
+            # default result file generated by OM
+            self.resultfile = self.tempdir / f"{self.modelName}_res.mat"
+        elif os.path.exists(resultfile):
+            self.resultfile = pathlib.Path(resultfile)
+        else:
+            self.resultfile = self.tempdir / resultfile
+
+        om_cmd = self.simulate_cmd(resultfile=self.resultfile, simflags=simflags, simargs=simargs, timeout=timeout)
 
         # delete resultfile ...
         if self.resultfile.is_file():
@@ -992,166 +1034,258 @@ def getSolutions(self, varList=None, resultfile=None):  # 12
         return np_res
 
     @staticmethod
-    def _strip_space(name):
-        if isinstance(name, str):
-            return name.replace(" ", "")
-
-        if isinstance(name, list):
-            return [x.replace(" ", "") for x in name]
-
-        raise ModelicaSystemError("Unhandled input for strip_space()")
-
-    def setMethodHelper(self, args1, args2, args3, args4=None):
-        """
-        Helper function for setParameter(),setContinuous(),setSimulationOptions(),setLinearizationOption(),setOptimizationOption()
-        args1 - string or list of string given by user
-        args2 - dict() containing the values of different variables(eg:, parameter,continuous,simulation parameters)
-        args3 - function name (eg; continuous, parameter, simulation, linearization,optimization)
-        args4 - dict() which stores the new override variables list,
-        """
-        def apply_single(args1):
-            args1 = self._strip_space(args1)
-            value = args1.split("=")
-            if value[0] in args2:
-                if args3 == "parameter" and self.isParameterChangeable(value[0], value[1]):
-                    args2[value[0]] = value[1]
-                    if args4 is not None:
-                        args4[value[0]] = value[1]
-                elif args3 != "parameter":
-                    args2[value[0]] = value[1]
-                    if args4 is not None:
-                        args4[value[0]] = value[1]
-
-                return True
+    def _prepare_input_data(
+            raw_input: str | list[str] | dict[str, Any],
+    ) -> dict[str, str]:
+        """
+        Convert raw input to a structured dictionary {'key1': 'value1', 'key2': 'value2'}.
+        """
 
-            else:
+        def prepare_str(str_in: str) -> dict[str, str]:
+            str_in = str_in.replace(" ", "")
+            key_val_list: list[str] = str_in.split("=")
+            if len(key_val_list) != 2:
+                raise ModelicaSystemError(f"Invalid 'key=value' pair: {str_in}")
+
+            input_data_from_str: dict[str, str] = {key_val_list[0]: key_val_list[1]}
+
+            return input_data_from_str
+
+        input_data: dict[str, str] = {}
+
+        if isinstance(raw_input, str):
+            warnings.warn(message="The definition of values to set should use a dictionary, "
+                                  "i.e. {'key1': 'val1', 'key2': 'val2', ...}. Please convert all cases which "
+                                  "use a string ('key=val') or list ['key1=val1', 'key2=val2', ...]",
+                          category=DeprecationWarning,
+                          stacklevel=3)
+            return prepare_str(raw_input)
+
+        if isinstance(raw_input, list):
+            warnings.warn(message="The definition of values to set should use a dictionary, "
+                                  "i.e. {'key1': 'val1', 'key2': 'val2', ...}. Please convert all cases which "
+                                  "use a string ('key=val') or list ['key1=val1', 'key2=val2', ...]",
+                          category=DeprecationWarning,
+                          stacklevel=3)
+
+            for item in raw_input:
+                input_data |= prepare_str(item)
+
+            return input_data
+
+        if isinstance(raw_input, dict):
+            for key, val in raw_input.items():
+                # convert all values to strings to align it on one type: dict[str, str]
+                # spaces have to be removed as setInput() could take list of tuples as input and spaces would
+                str_val = str(val).replace(' ', '')
+                if ' ' in key or ' ' in str_val:
+                    raise ModelicaSystemError(f"Spaces not allowed in key/value pairs: {repr(key)} = {repr(val)}!")
+                input_data[key] = str_val
+
+            return input_data
+
+        raise ModelicaSystemError(f"Invalid type of input: {type(raw_input)}")
+
+    def _set_method_helper(
+            self,
+            inputdata: dict[str, str],
+            classdata: dict[str, Any],
+            datatype: str,
+            overwritedata: Optional[dict[str, str]] = None,
+    ) -> bool:
+        """
+        Helper function for:
+        * setParameter()
+        * setContinuous()
+        * setSimulationOptions()
+        * setLinearizationOption()
+        * setOptimizationOption()
+        * setInputs()
+
+        Parameters
+        ----------
+        inputdata
+            string or list of string given by user
+        classdata
+            dict() containing the values of different variables (eg: parameter, continuous, simulation parameters)
+        datatype
+            type identifier (eg; continuous, parameter, simulation, linearization, optimization)
+        overwritedata
+            dict() which stores the new override variables list,
+        """
+
+        inputdata_status: dict[str, bool] = {}
+        for key, val in inputdata.items():
+            if key not in classdata:
                 raise ModelicaSystemError("Unhandled case in setMethodHelper.apply_single() - "
-                                          f"{repr(value[0])} is not a {repr(args3)} variable")
+                                          f"{repr(key)} is not a {repr(datatype)} variable")
+
+            status = False
+            if datatype == "parameter" and not self.isParameterChangeable(key):
+                logger.debug(f"It is not possible to set the parameter {repr(key)}. It seems to be "
+                             "structural, final, protected, evaluated or has a non-constant binding. "
+                             "Use sendExpression(...) and rebuild the model using buildModel() API; example: "
+                             "sendExpression(\"setParameterValue("
+                             f"{self.modelName}, {key}, {val if val is not None else '<?value?>'}"
+                             ")\") ")
+            else:
+                classdata[key] = val
+                if overwritedata is not None:
+                    overwritedata[key] = val
+                status = True
 
-        result = []
-        if isinstance(args1, str):
-            result = [apply_single(args1)]
+            inputdata_status[key] = status
 
-        elif isinstance(args1, list):
-            result = []
-            args1 = self._strip_space(args1)
-            for var in args1:
-                result.append(apply_single(var))
+        return all(inputdata_status.values())
 
-        return all(result)
+    def isParameterChangeable(
+            self,
+            name: str,
+    ) -> bool:
+        q = self.getQuantities(name)
+        if q[0]["changeable"] == "false":
+            return False
+        return True
 
-    def setContinuous(self, cvals):  # 13
+    def setContinuous(self, cvals: str | list[str] | dict[str, Any]) -> bool:
         """
         This method is used to set continuous values. It can be called:
         with a sequence of continuous name and assigning corresponding values as arguments as show in the example below:
         usage
-        >>> setContinuous("Name=value")
-        >>> setContinuous(["Name1=value1","Name2=value2"])
+        >>> setContinuous("Name=value")  # depreciated
+        >>> setContinuous(["Name1=value1","Name2=value2"])  # depreciated
+        >>> setContinuous(cvals={"Name1": "value1", "Name2": "value2"})
         """
-        return self.setMethodHelper(cvals, self.continuouslist, "continuous", self.overridevariables)
+        inputdata = self._prepare_input_data(raw_input=cvals)
+
+        return self._set_method_helper(
+            inputdata=inputdata,
+            classdata=self.continuouslist,
+            datatype="continuous",
+            overwritedata=self.overridevariables)
 
-    def setParameters(self, pvals):  # 14
+    def setParameters(self, pvals: str | list[str] | dict[str, Any]) -> bool:
         """
         This method is used to set parameter values. It can be called:
         with a sequence of parameter name and assigning corresponding value as arguments as show in the example below:
         usage
-        >>> setParameters("Name=value")
-        >>> setParameters(["Name1=value1","Name2=value2"])
+        >>> setParameters("Name=value")  # depreciated
+        >>> setParameters(["Name1=value1","Name2=value2"])  # depreciated
+        >>> setParameters(pvals={"Name1": "value1", "Name2": "value2"})
         """
-        return self.setMethodHelper(pvals, self.paramlist, "parameter", self.overridevariables)
+        inputdata = self._prepare_input_data(raw_input=pvals)
 
-    def isParameterChangeable(self, name, value):
-        q = self.getQuantities(name)
-        if q[0]["changeable"] == "false":
-            logger.debug(f"setParameters() failed : It is not possible to set the following signal {repr(name)}. "
-                         "It seems to be structural, final, protected or evaluated or has a non-constant binding, "
-                         f"use sendExpression(\"setParameterValue({self.modelName}, {name}, {value})\") "
-                         "and rebuild the model using buildModel() API")
-            return False
-        return True
+        return self._set_method_helper(
+            inputdata=inputdata,
+            classdata=self.paramlist,
+            datatype="parameter",
+            overwritedata=self.overridevariables)
 
-    def setSimulationOptions(self, simOptions):  # 16
+    def setSimulationOptions(self, simOptions: str | list[str] | dict[str, Any]) -> bool:
         """
         This method is used to set simulation options. It can be called:
         with a sequence of simulation options name and assigning corresponding values as arguments as show in the example below:
         usage
-        >>> setSimulationOptions("Name=value")
-        >>> setSimulationOptions(["Name1=value1","Name2=value2"])
+        >>> setSimulationOptions("Name=value")  # depreciated
+        >>> setSimulationOptions(["Name1=value1","Name2=value2"])  # depreciated
+        >>> setSimulationOptions(simOptions={"Name1": "value1", "Name2": "value2"})
         """
-        return self.setMethodHelper(simOptions, self.simulateOptions, "simulation-option", self.simoptionsoverride)
+        inputdata = self._prepare_input_data(raw_input=simOptions)
 
-    def setLinearizationOptions(self, linearizationOptions):  # 18
+        return self._set_method_helper(
+            inputdata=inputdata,
+            classdata=self.simulateOptions,
+            datatype="simulation-option",
+            overwritedata=self.simoptionsoverride)
+
+    def setLinearizationOptions(self, linearizationOptions: str | list[str] | dict[str, Any]) -> bool:
         """
         This method is used to set linearization options. It can be called:
         with a sequence of linearization options name and assigning corresponding value as arguments as show in the example below
         usage
-        >>> setLinearizationOptions("Name=value")
-        >>> setLinearizationOptions(["Name1=value1","Name2=value2"])
+        >>> setLinearizationOptions("Name=value")  # depreciated
+        >>> setLinearizationOptions(["Name1=value1","Name2=value2"])  # depreciated
+        >>> setLinearizationOptions(linearizationOtions={"Name1": "value1", "Name2": "value2"})
         """
-        return self.setMethodHelper(linearizationOptions, self.linearOptions, "Linearization-option", None)
+        inputdata = self._prepare_input_data(raw_input=linearizationOptions)
+
+        return self._set_method_helper(
+            inputdata=inputdata,
+            classdata=self.linearOptions,
+            datatype="Linearization-option",
+            overwritedata=None)
 
-    def setOptimizationOptions(self, optimizationOptions):  # 17
+    def setOptimizationOptions(self, optimizationOptions: str | list[str] | dict[str, Any]) -> bool:
         """
         This method is used to set optimization options. It can be called:
         with a sequence of optimization options name and assigning corresponding values as arguments as show in the example below:
         usage
-        >>> setOptimizationOptions("Name=value")
-        >>> setOptimizationOptions(["Name1=value1","Name2=value2"])
+        >>> setOptimizationOptions("Name=value")  # depreciated
+        >>> setOptimizationOptions(["Name1=value1","Name2=value2"])  # depreciated
+        >>> setOptimizationOptions(optimizationOptions={"Name1": "value1", "Name2": "value2"})
         """
-        return self.setMethodHelper(optimizationOptions, self.optimizeOptions, "optimization-option", None)
+        inputdata = self._prepare_input_data(raw_input=optimizationOptions)
+
+        return self._set_method_helper(
+            inputdata=inputdata,
+            classdata=self.optimizeOptions,
+            datatype="optimization-option",
+            overwritedata=None)
 
-    def setInputs(self, name):  # 15
+    def setInputs(self, name: str | list[str] | dict[str, Any]) -> bool:
         """
-        This method is used to set input values. It can be called:
-        with a sequence of input name and assigning corresponding values as arguments as show in the example below:
-        usage
-        >>> setInputs("Name=value")
-        >>> setInputs(["Name1=value1","Name2=value2"])
-        """
-        if isinstance(name, str):
-            name = self._strip_space(name)
-            value = name.split("=")
-            if value[0] in self.inputlist:
-                tmpvalue = eval(value[1])
-                if isinstance(tmpvalue, (int, float)):
-                    self.inputlist[value[0]] = [(float(self.simulateOptions["startTime"]), float(value[1])),
-                                                (float(self.simulateOptions["stopTime"]), float(value[1]))]
-                elif isinstance(tmpvalue, list):
-                    self.checkValidInputs(tmpvalue)
-                    self.inputlist[value[0]] = tmpvalue
-                self.inputFlag = True
-            else:
-                raise ModelicaSystemError(f"{value[0]} is not an input")
-        elif isinstance(name, list):
-            name = self._strip_space(name)
-            for var in name:
-                value = var.split("=")
-                if value[0] in self.inputlist:
-                    tmpvalue = eval(value[1])
-                    if isinstance(tmpvalue, (int, float)):
-                        self.inputlist[value[0]] = [(float(self.simulateOptions["startTime"]), float(value[1])),
-                                                    (float(self.simulateOptions["stopTime"]), float(value[1]))]
-                    elif isinstance(tmpvalue, list):
-                        self.checkValidInputs(tmpvalue)
-                        self.inputlist[value[0]] = tmpvalue
-                    self.inputFlag = True
-                else:
-                    raise ModelicaSystemError(f"{value[0]} is not an input!")
-
-    def checkValidInputs(self, name):
-        if name != sorted(name, key=lambda x: x[0]):
-            raise ModelicaSystemError('Time value should be in increasing order')
-        for l in name:
-            if isinstance(l, tuple):
-                # if l[0] < float(self.simValuesList[0]):
-                if l[0] < float(self.simulateOptions["startTime"]):
-                    raise ModelicaSystemError('Input time value is less than simulation startTime')
-                if len(l) != 2:
-                    raise ModelicaSystemError(f'Value for {l} is in incorrect format!')
+        This method is used to set input values. It can be called with a sequence of input name and assigning
+        corresponding values as arguments as show in the example below. Compared to other set*() methods this is a
+        special case as value could be a list of tuples - these are converted to a string in _prepare_input_data()
+        and restored here via ast.literal_eval().
+
+        >>> setInputs("Name=value")  # depreciated
+        >>> setInputs(["Name1=value1","Name2=value2"])  # depreciated
+        >>> setInputs(name={"Name1": "value1", "Name2": "value2"})
+        """
+        inputdata = self._prepare_input_data(raw_input=name)
+
+        for key, val in inputdata.items():
+            if key not in self.inputlist:
+                raise ModelicaSystemError(f"{key} is not an input")
+
+            if not isinstance(val, str):
+                raise ModelicaSystemError(f"Invalid data in input for {repr(key)}: {repr(val)}")
+
+            val_evaluated = ast.literal_eval(val)
+
+            if isinstance(val_evaluated, (int, float)):
+                self.inputlist[key] = [(float(self.simulateOptions["startTime"]), float(val)),
+                                       (float(self.simulateOptions["stopTime"]), float(val))]
+            elif isinstance(val_evaluated, list):
+                if not all([isinstance(item, tuple) for item in val_evaluated]):
+                    raise ModelicaSystemError("Value for setInput() must be in tuple format; "
+                                              f"got {repr(val_evaluated)}")
+                if val_evaluated != sorted(val_evaluated, key=lambda x: x[0]):
+                    raise ModelicaSystemError("Time value should be in increasing order; "
+                                              f"got {repr(val_evaluated)}")
+
+                for item in val_evaluated:
+                    if item[0] < float(self.simulateOptions["startTime"]):
+                        raise ModelicaSystemError(f"Time value in {repr(item)} of {repr(val_evaluated)} is less "
+                                                  "than the simulation start time")
+                    if len(item) != 2:
+                        raise ModelicaSystemError(f"Value {repr(item)} of {repr(val_evaluated)} "
+                                                  "is in incorrect format!")
+
+                self.inputlist[key] = val_evaluated
             else:
-                raise ModelicaSystemError('Error!!! Value must be in tuple format')
+                raise ModelicaSystemError(f"Data cannot be evaluated for {repr(key)}: {repr(val)}")
+
+            self.inputFlag = True
 
-    def createCSVData(self) -> pathlib.Path:
+        return True
+
+    def createCSVData(self, csvfile: Optional[pathlib.Path] = None) -> pathlib.Path:
+        """
+        Create a csv file with inputs for the simulation/optimization of the model. If csvfile is provided as argument,
+        this file is used; else a generic file name is created.
+        """
         start_time: float = float(self.simulateOptions["startTime"])
         stop_time: float = float(self.simulateOptions["stopTime"])
 
@@ -1192,13 +1326,14 @@ def createCSVData(self) -> pathlib.Path:
             ]
             csv_rows.append(row)
 
-        csvFile = self.tempdir / f'{self.modelName}.csv'
+        if csvfile is None:
+            csvfile = self.tempdir / f'{self.modelName}.csv'
 
-        with open(file=csvFile, mode="w", encoding="utf-8", newline="") as fh:
+        with open(file=csvfile, mode="w", encoding="utf-8", newline="") as fh:
             writer = csv.writer(fh)
             writer.writerows(csv_rows)
 
-        return csvFile
+        return csvfile
 
     # to convert Modelica model to FMU
     def convertMo2Fmu(self, version="2.0", fmuType="me_cs", fileNamePrefix="<default>", includeResources=True):  # 19
@@ -1314,8 +1449,8 @@ def load_module_from_path(module_name, file_path):
                     for l in tupleList:
                         if l[0] < float(self.simulateOptions["startTime"]):
                             raise ModelicaSystemError('Input time value is less than simulation startTime')
-            self.csvFile = self.createCSVData()
-            om_cmd.arg_set(key="csvInput", val=self.csvFile.as_posix())
+            csvfile = self.createCSVData()
+            om_cmd.arg_set(key="csvInput", val=csvfile.as_posix())
 
         om_cmd.arg_set(key="l", val=str(lintime or self.linearOptions["stopTime"]))
 
@@ -1382,3 +1517,373 @@ def getLinearStates(self):
         >>> getLinearStates()
         """
         return self.linearstates
+
+
+class ModelicaSystemDoE:
+    """
+    Class to run DoEs based on a (Open)Modelica model using ModelicaSystem
+
+    Example
+    -------
+    ```
+    import OMPython
+    import pathlib
+
+
+    def run_doe():
+        mypath = pathlib.Path('.')
+
+        model = mypath / "M.mo"
+        model.write_text(
+            "    model M\n"
+            "      parameter Integer p=1;\n"
+            "      parameter Integer q=1;\n"
+            "      parameter Real a = -1;\n"
+            "      parameter Real b = -1;\n"
+            "      Real x[p];\n"
+            "      Real y[q];\n"
+            "    equation\n"
+            "      der(x) = a * fill(1.0, p);\n"
+            "      der(y) = b * fill(1.0, q);\n"
+            "    end M;\n"
+        )
+
+        param = {
+            # structural
+            'p': [1, 2],
+            'q': [3, 4],
+            # simple
+            'a': [5, 6],
+            'b': [7, 8],
+        }
+
+        resdir = mypath / 'DoE'
+        resdir.mkdir(exist_ok=True)
+
+        doe_mod = OMPython.ModelicaSystemDoE(
+            fileName=model.as_posix(),
+            modelName="M",
+            parameters=param,
+            resultpath=resdir,
+            simargs={"override": {'stopTime': 1.0}},
+        )
+        doe_mod.prepare()
+        doe_dict = doe_mod.get_doe()
+        doe_mod.simulate()
+        doe_sol = doe_mod.get_solutions()
+
+        # ... work with doe_df and doe_sol ...
+
+
+    if __name__ == "__main__":
+        run_doe()
+    ```
+
+    """
+
+    DICT_RESULT_FILENAME: str = 'result filename'
+    DICT_RESULT_AVAILABLE: str = 'result available'
+
+    def __init__(
+            self,
+            fileName: Optional[str | os.PathLike | pathlib.Path] = None,
+            modelName: Optional[str] = None,
+            lmodel: Optional[list[str | tuple[str, str]]] = None,
+            commandLineOptions: Optional[str] = None,
+            variableFilter: Optional[str] = None,
+            customBuildDirectory: Optional[str | os.PathLike | pathlib.Path] = None,
+            omhome: Optional[str] = None,
+
+            simargs: Optional[dict[str, Optional[str | dict[str, str]]]] = None,
+            timeout: Optional[int] = None,
+
+            resultpath: Optional[pathlib.Path] = None,
+            parameters: Optional[dict[str, list[str] | list[int] | list[float]]] = None,
+    ) -> None:
+        """
+        Initialisation of ModelicaSystemDoE. The parameters are based on: ModelicaSystem.__init__() and
+        ModelicaSystem.simulate(). Additionally, the path to store the result files is needed (= resultpath) as well as
+        a list of parameters to vary for the Doe (= parameters). All possible combinations are considered.
+        """
+        self._lmodel = lmodel
+        self._modelName = modelName
+        self._fileName = fileName
+
+        self._CommandLineOptions = commandLineOptions
+        self._variableFilter = variableFilter
+        self._customBuildDirectory = customBuildDirectory
+        self._omhome = omhome
+
+        # reference for the model; not used for any simulations but to evaluate parameters, etc.
+        self._mod = ModelicaSystem(
+            fileName=self._fileName,
+            modelName=self._modelName,
+            lmodel=self._lmodel,
+            commandLineOptions=self._CommandLineOptions,
+            variableFilter=self._variableFilter,
+            customBuildDirectory=self._customBuildDirectory,
+            omhome=self._omhome,
+        )
+
+        self._simargs = simargs
+        self._timeout = timeout
+
+        if isinstance(resultpath, pathlib.Path):
+            self._resultpath = resultpath
+        else:
+            self._resultpath = pathlib.Path('.')
+
+        if isinstance(parameters, dict):
+            self._parameters = parameters
+        else:
+            self._parameters = {}
+
+        self._sim_dict: Optional[dict[str, dict[str, Any]]] = None
+        self._sim_task_query: queue.Queue = queue.Queue()
+
+    def prepare(self) -> int:
+        """
+        Prepare the DoE by evaluating the parameters. Each structural parameter requires a new instance of
+        ModelicaSystem while the non-structural parameters can just be set on the executable.
+
+        The return value is the number of simulation defined.
+        """
+
+        param_structure = {}
+        param_simple = {}
+        for param_name in self._parameters.keys():
+            changeable = self._mod.isParameterChangeable(name=param_name)
+            logger.info(f"Parameter {repr(param_name)} is changeable? {changeable}")
+
+            if changeable:
+                param_simple[param_name] = self._parameters[param_name]
+            else:
+                param_structure[param_name] = self._parameters[param_name]
+
+        param_structure_combinations = list(itertools.product(*param_structure.values()))
+        param_simple_combinations = list(itertools.product(*param_simple.values()))
+
+        self._sim_dict = {}
+        for idx_pc_structure, pc_structure in enumerate(param_structure_combinations):
+            mod_structure = ModelicaSystem(
+                fileName=self._fileName,
+                modelName=self._modelName,
+                lmodel=self._lmodel,
+                commandLineOptions=self._CommandLineOptions,
+                variableFilter=self._variableFilter,
+                customBuildDirectory=self._customBuildDirectory,
+                omhome=self._omhome,
+                build=False,
+            )
+
+            sim_param_structure = {}
+            for idx_structure, pk_structure in enumerate(param_structure.keys()):
+                sim_param_structure[pk_structure] = pc_structure[idx_structure]
+
+                pk_value = pc_structure[idx_structure]
+                if isinstance(pk_value, str):
+                    expression = f"setParameterValue({self._modelName}, {pk_structure}, \"{pk_value}\")"
+                elif isinstance(pk_value, bool):
+                    pk_value_bool_str = "true" if pk_value else "false"
+                    expression = f"setParameterValue({self._modelName}, {pk_structure}, {pk_value_bool_str});"
+                else:
+                    expression = f"setParameterValue({self._modelName}, {pk_structure}, {pk_value})"
+                res = mod_structure.sendExpression(expression)
+                if not res:
+                    raise ModelicaSystemError(f"Cannot set structural parameter {self._modelName}.{pk_structure} "
+                                              f"to {pk_value} using {repr(expression)}")
+
+            mod_structure.buildModel(variableFilter=self._variableFilter)
+
+            for idx_pc_simple, pc_simple in enumerate(param_simple_combinations):
+                sim_param_simple = {}
+                for idx_simple, pk_simple in enumerate(param_simple.keys()):
+                    sim_param_simple[pk_simple] = pc_simple[idx_simple]
+
+                resfilename = f"DOE_{idx_pc_structure:09d}_{idx_pc_simple:09d}.mat"
+                logger.info(f"use result file {repr(resfilename)} "
+                            f"for structural parameters: {sim_param_structure} "
+                            f"and simple parameters: {sim_param_simple}")
+                resultfile = self._resultpath / resfilename
+
+                df_data = (
+                        {
+                            'ID structure': idx_pc_structure,
+                        }
+                        | sim_param_structure
+                        | {
+                            'ID non-structure': idx_pc_simple,
+                        }
+                        | sim_param_simple
+                        | {
+                            self.DICT_RESULT_AVAILABLE: False,
+                        }
+                )
+
+                self._sim_dict[resfilename] = df_data
+
+                mscmd = mod_structure.simulate_cmd(
+                    resultfile=resultfile.absolute().resolve(),
+                    timeout=self._timeout,
+                )
+                if self._simargs is not None:
+                    mscmd.args_set(args=self._simargs)
+                mscmd.args_set(args={"override": sim_param_simple})
+
+                self._sim_task_query.put(mscmd)
+
+        logger.info(f"Prepared {self._sim_task_query.qsize()} simulation definitions for the defined DoE.")
+
+        return self._sim_task_query.qsize()
+
+    def get_doe(self) -> Optional[dict[str, dict[str, Any]]]:
+        """
+        Get the defined DoE as a dict, where each key is the result filename and the value is a dict of simulation
+        settings including structural and non-structural parameters.
+
+        The following code snippet can be used to convert the data to a pandas dataframe:
+
+        ```
+        import pandas as pd
+
+        doe_dict = doe_mod.get_doe()
+        doe_df = pd.DataFrame.from_dict(data=doe_dict, orient='index')
+        ```
+
+        """
+        return self._sim_dict
+
+    def simulate(
+            self,
+            num_workers: int = 3,
+    ) -> bool:
+        """
+        Simulate the DoE using the defined number of workers.
+
+        Returns True if all simulations were done successfully, else False.
+        """
+
+        sim_query_total = self._sim_task_query.qsize()
+        if not isinstance(self._sim_dict, dict) or len(self._sim_dict) == 0:
+            raise ModelicaSystemError("Missing Doe Summary!")
+        sim_dict_total = len(self._sim_dict)
+
+        def worker(worker_id, task_queue):
+            while True:
+                try:
+                    # Get the next task from the queue
+                    mscmd = task_queue.get(block=False)
+                except queue.Empty:
+                    logger.info(f"[Worker {worker_id}] No more simulations to run.")
+                    break
+
+                if mscmd is None:
+                    raise ModelicaSystemError("Missing simulation definition!")
+
+                resultfile = mscmd.arg_get(key='r')
+                resultpath = pathlib.Path(resultfile)
+
+                logger.info(f"[Worker {worker_id}] Performing task: {resultpath.name}")
+
+                try:
+                    mscmd.run()
+                except ModelicaSystemError as ex:
+                    logger.warning(f"Simulation error for {resultpath.name}: {ex}")
+
+                # Mark the task as done
+                task_queue.task_done()
+
+                sim_query_done = sim_query_total - self._sim_task_query.qsize()
+                logger.info(f"[Worker {worker_id}] Task completed: {resultpath.name} "
+                            f"({sim_query_total - sim_query_done}/{sim_query_total} = "
+                            f"{(sim_query_total - sim_query_done) / sim_query_total * 100:.2f}% of tasks left)")
+
+        logger.info(f"Start simulations for DoE with {sim_query_total} simulations "
+                    f"using {num_workers} workers ...")
+
+        # Create and start worker threads
+        threads = []
+        for i in range(num_workers):
+            thread = threading.Thread(target=worker, args=(i, self._sim_task_query))
+            thread.start()
+            threads.append(thread)
+
+        # Wait for all threads to complete
+        for thread in threads:
+            thread.join()
+
+        sim_dict_done = 0
+        for resultfilename in self._sim_dict:
+            resultfile = self._resultpath / resultfilename
+
+            # include check for an empty (=> 0B) result file which indicates a crash of the model executable
+            # see: https://github.com/OpenModelica/OMPython/issues/261
+            # https://github.com/OpenModelica/OpenModelica/issues/13829
+            if resultfile.is_file() and resultfile.stat().st_size > 0:
+                self._sim_dict[resultfilename][self.DICT_RESULT_AVAILABLE] = True
+                sim_dict_done += 1
+
+        logger.info(f"All workers finished ({sim_dict_done} of {sim_dict_total} simulations with a result file).")
+
+        return sim_dict_total == sim_dict_done
+
+    def get_solutions(
+            self,
+            var_list: Optional[list] = None,
+    ) -> Optional[tuple[str] | dict[str, dict[str, np.ndarray]]]:
+        """
+        Get all solutions of the DoE run. The following return values are possible:
+
+        * A list of variables if val_list == None
+
+        * The Solutions as dict[str, pd.DataFrame] if a value list (== val_list) is defined.
+
+        The following code snippet can be used to convert the solution data for each run to a pandas dataframe:
+
+        ```
+        import pandas as pd
+
+        doe_sol = doe_mod.get_solutions()
+        for key in doe_sol:
+            data = doe_sol[key]['data']
+            if data:
+                doe_sol[key]['df'] = pd.DataFrame.from_dict(data=data)
+            else:
+                doe_sol[key]['df'] = None
+        ```
+
+        """
+        if not isinstance(self._sim_dict, dict):
+            return None
+
+        if len(self._sim_dict) == 0:
+            raise ModelicaSystemError("No result files available - all simulations did fail?")
+
+        sol_dict: dict[str, dict[str, Any]] = {}
+        for resultfilename in self._sim_dict:
+            resultfile = self._resultpath / resultfilename
+
+            sol_dict[resultfilename] = {}
+
+            if not self._sim_dict[resultfilename][self.DICT_RESULT_AVAILABLE]:
+                sol_dict[resultfilename]['msg'] = 'No result file available!'
+                sol_dict[resultfilename]['data'] = {}
+                continue
+
+            if var_list is None:
+                var_list_row = list(self._mod.getSolutions(resultfile=resultfile))
+            else:
+                var_list_row = var_list
+
+            try:
+                sol = self._mod.getSolutions(varList=var_list_row, resultfile=resultfile)
+                sol_data = {var: sol[idx] for idx, var in enumerate(var_list_row)}
+                sol_dict[resultfilename]['msg'] = 'Simulation available'
+                sol_dict[resultfilename]['data'] = sol_data
+            except ModelicaSystemError as ex:
+                msg = f"Error reading solution for {resultfilename}: {ex}"
+                logger.warning(msg)
+                sol_dict[resultfilename]['msg'] = msg
+                sol_dict[resultfilename]['data'] = {}
+
+        return sol_dict
diff --git a/OMPython/__init__.py b/OMPython/__init__.py
index 1da0a0a3..8a0584fd 100644
--- a/OMPython/__init__.py
+++ b/OMPython/__init__.py
@@ -36,7 +36,8 @@
  CONDITIONS OF OSMC-PL.
 """
 
-from OMPython.ModelicaSystem import LinearizationResult, ModelicaSystem, ModelicaSystemCmd, ModelicaSystemError
+from OMPython.ModelicaSystem import (LinearizationResult, ModelicaSystem, ModelicaSystemCmd, ModelicaSystemDoE,
+                                     ModelicaSystemError)
 from OMPython.OMCSession import (OMCSessionCmd, OMCSessionException, OMCSessionZMQ,
                                  OMCProcessPort, OMCProcessLocal, OMCProcessDocker, OMCProcessDockerContainer,
                                  OMCProcessWSL)
@@ -46,6 +47,7 @@
     'LinearizationResult',
     'ModelicaSystem',
     'ModelicaSystemCmd',
+    'ModelicaSystemDoE',
     'ModelicaSystemError',
 
     'OMCSessionCmd',
diff --git a/tests/test_ModelicaSystem.py b/tests/test_ModelicaSystem.py
index 156dde03..b3026279 100644
--- a/tests/test_ModelicaSystem.py
+++ b/tests/test_ModelicaSystem.py
@@ -35,8 +35,8 @@ def test_setParameters():
     mod = OMPython.ModelicaSystem(model_path + "BouncingBall.mo", "BouncingBall")
 
     # method 1
-    mod.setParameters("e=1.234")
-    mod.setParameters("g=321.0")
+    mod.setParameters(pvals={"e": 1.234})
+    mod.setParameters(pvals={"g": 321.0})
     assert mod.getParameters("e") == ["1.234"]
     assert mod.getParameters("g") == ["321.0"]
     assert mod.getParameters() == {
@@ -47,7 +47,7 @@ def test_setParameters():
         mod.getParameters("thisParameterDoesNotExist")
 
     # method 2
-    mod.setParameters(["e=21.3", "g=0.12"])
+    mod.setParameters(pvals={"e": 21.3, "g": 0.12})
     assert mod.getParameters() == {
         "e": "21.3",
         "g": "0.12",
@@ -64,8 +64,8 @@ def test_setSimulationOptions():
     mod = OMPython.ModelicaSystem(fileName=model_path + "BouncingBall.mo", modelName="BouncingBall")
 
     # method 1
-    mod.setSimulationOptions("stopTime=1.234")
-    mod.setSimulationOptions("tolerance=1.1e-08")
+    mod.setSimulationOptions(simOptions={"stopTime": 1.234})
+    mod.setSimulationOptions(simOptions={"tolerance": 1.1e-08})
     assert mod.getSimulationOptions("stopTime") == ["1.234"]
     assert mod.getSimulationOptions("tolerance") == ["1.1e-08"]
     assert mod.getSimulationOptions(["tolerance", "stopTime"]) == ["1.1e-08", "1.234"]
@@ -77,7 +77,7 @@ def test_setSimulationOptions():
         mod.getSimulationOptions("thisOptionDoesNotExist")
 
     # method 2
-    mod.setSimulationOptions(["stopTime=2.1", "tolerance=1.2e-08"])
+    mod.setSimulationOptions(simOptions={"stopTime": 2.1, "tolerance": "1.2e-08"})
     d = mod.getSimulationOptions()
     assert d["stopTime"] == "2.1"
     assert d["tolerance"] == "1.2e-08"
@@ -119,7 +119,7 @@ def test_getSolutions(model_firstorder):
     a = -1
     tau = -1 / a
     stopTime = 5*tau
-    mod.setSimulationOptions([f"stopTime={stopTime}", "stepSize=0.1", "tolerance=1e-8"])
+    mod.setSimulationOptions(simOptions={"stopTime": stopTime, "stepSize": 0.1, "tolerance": 1e-8})
     mod.simulate()
 
     x = mod.getSolutions("x")
@@ -298,7 +298,7 @@ def test_getters(tmp_path):
     x0 = 1.0
     x_analytical = -b/a + (x0 + b/a) * np.exp(a * stopTime)
     dx_analytical = (x0 + b/a) * a * np.exp(a * stopTime)
-    mod.setSimulationOptions(f"stopTime={stopTime}")
+    mod.setSimulationOptions(simOptions={"stopTime": stopTime})
     mod.simulate()
 
     # getOutputs after simulate()
@@ -327,7 +327,7 @@ def test_getters(tmp_path):
         mod.getContinuous("a")  # a is a parameter
 
     with pytest.raises(OMPython.ModelicaSystemError):
-        mod.setSimulationOptions("thisOptionDoesNotExist=3")
+        mod.setSimulationOptions(simOptions={"thisOptionDoesNotExist": 3})
 
 
 def test_simulate_inputs(tmp_path):
@@ -345,7 +345,7 @@ def test_simulate_inputs(tmp_path):
 """)
     mod = OMPython.ModelicaSystem(fileName=model_file.as_posix(), modelName="M_input")
 
-    mod.setSimulationOptions("stopTime=1.0")
+    mod.setSimulationOptions(simOptions={"stopTime": 1.0})
 
     # integrate zero (no setInputs call) - it should default to None -> 0
     assert mod.getInputs() == {
@@ -357,7 +357,7 @@ def test_simulate_inputs(tmp_path):
     assert np.isclose(y[-1], 0.0)
 
     # integrate a constant
-    mod.setInputs("u1=2.5")
+    mod.setInputs(name={"u1": 2.5})
     assert mod.getInputs() == {
         "u1": [
             (0.0, 2.5),
@@ -370,7 +370,7 @@ def test_simulate_inputs(tmp_path):
     assert np.isclose(y[-1], 2.5)
 
     # now let's integrate the sum of two ramps
-    mod.setInputs("u1=[(0.0, 0.0), (0.5, 2), (1.0, 0)]")
+    mod.setInputs(name={"u1": [(0.0, 0.0), (0.5, 2), (1.0, 0)]})
     assert mod.getInputs("u1") == [[
         (0.0, 0.0),
         (0.5, 2.0),
@@ -383,25 +383,25 @@ def test_simulate_inputs(tmp_path):
     # let's try some edge cases
     # unmatched startTime
     with pytest.raises(OMPython.ModelicaSystemError):
-        mod.setInputs("u1=[(-0.5, 0.0), (1.0, 1)]")
+        mod.setInputs(name={"u1": [(-0.5, 0.0), (1.0, 1)]})
         mod.simulate()
     # unmatched stopTime
     with pytest.raises(OMPython.ModelicaSystemError):
-        mod.setInputs("u1=[(0.0, 0.0), (0.5, 1)]")
+        mod.setInputs(name={"u1": [(0.0, 0.0), (0.5, 1)]})
         mod.simulate()
 
     # Let's use both inputs, but each one with different number of of
     # samples. This has an effect when generating the csv file.
-    mod.setInputs([
-        "u1=[(0.0, 0), (1.0, 1)]",
-        "u2=[(0.0, 0), (0.25, 0.5), (0.5, 1.0), (1.0, 0)]",
-    ])
-    mod.simulate()
-    assert pathlib.Path(mod.csvFile).read_text() == """time,u1,u2,end
+    mod.setInputs(name={"u1": [(0.0, 0), (1.0, 1)],
+                        "u2": [(0.0, 0), (0.25, 0.5), (0.5, 1.0), (1.0, 0)]})
+    csv_file = mod.createCSVData()
+    assert pathlib.Path(csv_file).read_text() == """time,u1,u2,end
 0.0,0.0,0.0,0
 0.25,0.25,0.5,0
 0.5,0.5,1.0,0
 1.0,1.0,0.0,0
 """
+
+    mod.simulate()
     y = mod.getSolutions("y")[0]
     assert np.isclose(y[-1], 1.0)
diff --git a/tests/test_ModelicaSystemDoE.py b/tests/test_ModelicaSystemDoE.py
new file mode 100644
index 00000000..40fed90d
--- /dev/null
+++ b/tests/test_ModelicaSystemDoE.py
@@ -0,0 +1,83 @@
+import numpy as np
+import OMPython
+import pathlib
+import pytest
+
+
+@pytest.fixture
+def model_doe(tmp_path: pathlib.Path) -> pathlib.Path:
+    # see: https://trac.openmodelica.org/OpenModelica/ticket/4052
+    mod = tmp_path / "M.mo"
+    mod.write_text("""
+model M
+  parameter Integer p=1;
+  parameter Integer q=1;
+  parameter Real a = -1;
+  parameter Real b = -1;
+  Real x[p];
+  Real y[q];
+equation
+  der(x) = a * fill(1.0, p);
+  der(y) = b * fill(1.0, q);
+end M;
+""")
+    return mod
+
+
+@pytest.fixture
+def param_doe() -> dict[str, list]:
+    param = {
+        # structural
+        'p': [1, 2],
+        'q': [3, 4],
+        # simple
+        'a': [5, 6],
+        'b': [7, 8],
+    }
+    return param
+
+
+def test_ModelicaSystemDoE(tmp_path, model_doe, param_doe):
+    tmpdir = tmp_path / 'DoE'
+    tmpdir.mkdir(exist_ok=True)
+
+    doe_mod = OMPython.ModelicaSystemDoE(
+        fileName=model_doe.as_posix(),
+        modelName="M",
+        parameters=param_doe,
+        resultpath=tmpdir,
+        simargs={"override": {'stopTime': 1.0}},
+    )
+    doe_count = doe_mod.prepare()
+    assert doe_count == 16
+
+    doe_dict = doe_mod.get_doe()
+    assert isinstance(doe_dict, dict)
+    assert len(doe_dict.keys()) == 16
+
+    doe_status = doe_mod.simulate()
+    assert doe_status is True
+
+    doe_sol = doe_mod.get_solutions()
+
+    for resultfilename in doe_dict:
+        row = doe_dict[resultfilename]
+
+        assert resultfilename in doe_sol
+        sol = doe_sol[resultfilename]
+
+        var_dict = {
+            # simple / non-structural parameters
+            'a': float(row['a']),
+            'b': float(row['b']),
+            # structural parameters
+            'p': float(row['p']),
+            'q': float(row['q']),
+            # variables using the structural parameters
+            f"x[{row['p']}]": float(row['a']),
+            f"y[{row['p']}]": float(row['b']),
+        }
+
+        for var in var_dict:
+            assert var in sol['data']
+            assert np.isclose(sol['data'][var][-1], var_dict[var])
diff --git a/tests/test_linearization.py b/tests/test_linearization.py
index 2c79190c..6af565c6 100644
--- a/tests/test_linearization.py
+++ b/tests/test_linearization.py
@@ -62,10 +62,10 @@ def test_getters(tmp_path):
     assert "startTime" in d
     assert "stopTime" in d
     assert mod.getLinearizationOptions(["stopTime", "startTime"]) == [d["stopTime"], d["startTime"]]
-    mod.setLinearizationOptions("stopTime=0.02")
+    mod.setLinearizationOptions(linearizationOptions={"stopTime": 0.02})
     assert mod.getLinearizationOptions("stopTime") == ["0.02"]
 
-    mod.setInputs(["u1=10", "u2=0"])
+    mod.setInputs(name={"u1": 10, "u2": 0})
     [A, B, C, D] = mod.linearize()
     g = float(mod.getParameters("g")[0])
     l = float(mod.getParameters("l")[0])
diff --git a/tests/test_optimization.py b/tests/test_optimization.py
index aa74df79..b4164397 100644
--- a/tests/test_optimization.py
+++ b/tests/test_optimization.py
@@ -35,13 +35,15 @@ def test_optimization_example(tmp_path):
 
     mod = OMPython.ModelicaSystem(fileName=model_file.as_posix(), modelName="BangBang2021")
 
-    mod.setOptimizationOptions(["numberOfIntervals=16", "stopTime=1",
-                                "stepSize=0.001", "tolerance=1e-8"])
+    mod.setOptimizationOptions(optimizationOptions={"numberOfIntervals": 16,
+                                                    "stopTime": 1,
+                                                    "stepSize": 0.001,
+                                                    "tolerance": 1e-8})
 
     # test the getter
     assert mod.getOptimizationOptions()["stopTime"] == "1"
     assert mod.getOptimizationOptions("stopTime") == ["1"]
-    assert mod.getOptimizationOptions(["tolerance", "stopTime"]) == ["1e-8", "1"]
+    assert mod.getOptimizationOptions(["tolerance", "stopTime"]) == ["1e-08", "1"]
 
     r = mod.optimize()
     # it is necessary to specify resultfile, otherwise it wouldn't find it.