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function diet()
% Copyright 2020, Gurobi Optimization, LLC
%
% Solve the classic diet model

% Nutrition guidelines, based on
% USDA Dietary Guidelines for Americans, 2005
% http://www.health.gov/DietaryGuidelines/dga2005/

ncategories = 4;

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#' get next best pilot stop using nearest neighbor
#'
#' @param tour 
#' @param dima 
#'
#' @return
#' @export
get_best_pilot_stop <- function(tour, dima) {
  stops <- 1:nrow(dima)
  potential_stops <- stops[-tour]

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# -*- coding: utf-8 -*-

# Form implementation generated from reading ui file 'addition_dialog.ui'
#
# Created by: PyQt5 UI code generator 5.15.4
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again.  Do not edit this file unless you know what you are doing.

from PyQt5 import QtCore, QtGui, QtWidgets

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<?xml version="1.0" encoding="UTF-8"?>
<ui version="4.0">
 <class>MainWindow</class>
 <widget class="QMainWindow" name="MainWindow">
  <property name="geometry">
   <rect>
    <x>0</x>
    <y>0</y>
    <width>452</width>
    <height>247</height>

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# -*- coding: utf-8 -*-

# Form implementation generated from reading ui file 'addition_dialog.ui'
#
# Created by: PyQt5 UI code generator 5.15.4
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again.  Do not edit this file unless you know what you are doing.

from PyQt5 import QtCore, QtGui, QtWidgets

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# source based on code presented here https://developers.google.com/optimization/mip/integer_opt

from ortools.linear_solver import pywraplp

# example how to solve a MIXED integer linear program continuous and integer variables
# x is continuous
# y is integer
def main():
    # Create the mip solver with the SCIP backend.
    solver = pywraplp.Solver.CreateSolver('SCIP')

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# source based on code presented here https://developers.google.com/optimization/mip/integer_opt

from ortools.linear_solver import pywraplp

# example how to solve a linear program having ONLY continuous variables
def main():
    # Create the mip solver with the SCIP backend.
    # HINT: GLOP cannot handle integer variables!
    solver = pywraplp.Solver.CreateSolver('GLOP')

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# source based on code presented here https://developers.google.com/optimization/mip/integer_opt

from ortools.linear_solver import pywraplp

# example how to solve a MIXED integer linear program continuous and integer variables
# and solve it with a gurobi solver installed (or gurobi cloud).
def main():
    # Create the mip solver with the SCIP backend.
    solver = pywraplp.Solver.CreateSolver('GUROBI_MIP')

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def remove_duplicate_candidates(N):
    '''
    Accept a list of candidate solutions (being binary lists) and make sure, each (element)-list is contained only once.

    :param N:
    :return: revised list containing no duplicate candidates
    '''
    unique_set = set(tuple(lst) for lst in N)
    unique_list_of_lists = [list(tpl) for tpl in unique_set]
    return unique_list_of_lists

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def construct_knapsack_solution(weights, values, capacity):
    '''
    Create a fair (initial) knapsack solution

    :param weights: Weights of the objects in the knapsack problem
    :param values: Values assigned to the knapsack items
    :param capacity: Capacity of the knapsack
    :return: an (initial) solution to start knapsack optimization with
    '''
    # enrich list with value/weight for each weight-value tupel