{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bonuses and merits\n", "\n", "## Problem definition\n", "A company pays a yearly bonus to employees based on the following indicators:\n", "\n", "1. **Performance:** Performance is evaluated by the direct manager using a quantitative method. The indicator is labelled as $CM_{1}$ and measured in a scale from 1 to 5.\n", "2. **Involvement:** Involvement in social media, events, coaching and other voluntary activities. Involvement is measured by the human resource department using a quantitative method. The indicator is labelled as $CM_{2}$ and measured in a scale from 1 to 5.\n", "3. **Professional development:** Professional development evaluates the accomplishment of training programs and courses suggested by the proximity manager. The indicator is labelled as $CM_{3}$.\n", "\n", "This year, the total score obtained among all employees is:\n", "\n", "$\\sum (𝑪𝑴_{𝟏}) = 𝟏𝟔𝟐𝟓$ Total points in performance\n", "\n", "$\\sum (𝑪𝑴_{2}) = 𝟏𝟒𝟎𝟗$ Total points in involvement\n", "\n", "$\\sum (𝑪𝑴_{3}) = 𝟏𝟑𝟖𝟕$ Total points in professional development\n", "\n", "The company wants to calculate bonuses using a linear expression weighting the three \n", "The board established the following rules:\n", "\n", "- No employee should receive more than 5000€ in bonuses\n", "- The value of performance is twice the value of involvement and professional development\n", "- The total budget for bonuses this year is 1.5M€\n", "\n", "Formulate an LPP that determines the optimal formula to calculate employee bonuses." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" }, "pycharm": { "stem_cell": { "cell_type": "raw", "source": [], "metadata": { "collapsed": false } } } }, "nbformat": 4, "nbformat_minor": 2 }