.Linear programming is one of the most widely used quantitative techniques for solving decision-making problems related to the optimal allocation of resources. A central concept within this field is duality, which describes a mathematical relationship between two equivalent formulations: the primal and the dual problem. Grasping this relationship not only enhances theoretical understanding of linear models but also facilitates detailed analysis of solutions and their sensitivity to changes in input data. This lecture is designed to explain how to systematically convert a linear program from its primal to its dual form, addressing both standard and mixed-form cases. It also explores how to derive the dual’s optimal solution table based on that of the primal, and vice versa.
- Enseignant: Ammar Hamioud
Linear programming is a powerful mathematical tool used to make optimal decisions in various fields such as production, transportation, distribution, and finance. Among the advanced methods for solving complex linear programming problems—especially those involving ≥ or = constraints—the Big M method stands out as a direct and effective approach. This method introduces artificial variables associated with a large constant denoted by M, enabling the transformation of the problem into a standard form that can be solved using the Simplex Method.
In this lecture, we will explore the Big M method, apply it to examples with mixed constraint formulations, and demonstrate how artificial variables are progressively removed from the basis to reach the optimal solution.
- Enseignant: Ammar Hamioud
Linear programming is considered one of the most important mathematical tools used to support decision-making, especially in areas related to maximizing profit or minimizing cost under certain constraints. Among the most effective methods for solving linear programming models, the Simplex Method stands out as a systematic approach that efficiently solves such problems. This lecture aims to introduce students to the fundamental principles of the Simplex method, how to convert linear programming models into standard form, and how to apply the procedural steps for solving using tabular techniques.
- Enseignant: Ammar Hamioud
Module Description
This module aims to develop students’ academic English skills required for research activities and master’s thesis writing in the field of Entrepreneurship. It focuses on reading, analyzing, and producing academic texts related to startups, innovation, business planning, and entrepreneurial strategies.
- Enseignant: M Bouhafs
Linear programming is considered one of the most important quantitative methods in decision-making, as it provides a precise mathematical framework for analyzing problems that involve the allocation of limited resources to achieve the best possible outcome—whether maximizing profit or minimizing cost. It is widely used across various fields such as economics, management, engineering, and industrial planning. Linear programming relies on mathematical models that express the relationships between variables using linear equations and inequalities. These models are built on key components such as the objective function and constraints and are often solved using graphical methods or algorithms like the Simplex method. This lecture aims to introduce students to the concepts of linear programming, its core components, and the steps involved in constructing and solving such models, supported by real-world examples that reinforce both theoretical understanding and practical application.
- Enseignant: Ammar Hamioud
Linear programming is considered one of the most important quantitative methods in decision-making, as it provides a precise mathematical framework for analyzing problems that involve the allocation of limited resources to achieve the best possible outcome—whether maximizing profit or minimizing cost. It is widely used across various fields such as economics, management, engineering, and industrial planning. Linear programming relies on mathematical models that express the relationships between variables using linear equations and inequalities. These models are built on key components such as the objective function and constraints and are often solved using graphical methods or algorithms like the Simplex method. This lecture aims to introduce students to the concepts of linear programming, its core components, and the steps involved in constructing and solving such models, supported by real-world examples that reinforce both theoretical understanding and practical application.
- Enseignant: Ammar Hamioud
In light of the growing challenges facing modern organizations, there is an increasing need to adopt tools and techniques that enable decision-making based on systematic analysis and quantitative data. Relying solely on personal experience or intuition is no longer sufficient to address the complex and ever-changing administrative issues, especially in an environment marked by fierce competition and economic and technological volatility. As a result, quantitative methods have emerged as one of the most important scientific alternatives, drawing upon mathematical models and statistical analysis to provide accurate, objective solutions that enhance the efficiency of managerial decisions.
This lecture aims to shed light on the general concept of quantitative methods in management, trace their historical development, explain the stages of model building, and highlight the key motivations behind their use in organizations. Additionally, it explores the most widely used quantitative techniques in decision-making and planning, emphasizing how these tools can contribute to better administrative outcomes and strategic performance.
- Enseignant: Ammar Hamioud
This course is designed to equip students with both the theoretical foundations and practical applications of quantitative tools used in administrative contexts. The course covers a wide range of topics, including linear programming, decision-making under risk, decision trees, cost analysis and break-even point, game theory, simulation, and dynamic programming. Special attention is given to understanding the contexts in which these models are applied, the conditions for their use, their limitations, and how to interpret results in ways that support accurate and effective decision-making.
The value of this course lies in its comprehensive nature and its integration with other modules within the Master’s program in Business Administration. Its applications go beyond production and operations management to include finance, marketing, human resource management, strategic planning, and quality control. Quantitative methods have become indispensable tools for enhancing organizational competitiveness, achieving operational efficiency, and ensuring sound decision-making in resource-constrained and uncertain environments.
Accordingly, mastering and properly applying quantitative methods represents a key step in shaping a modern manager capable of analyzing current realities, anticipating future developments, and formulating strategic decisions grounded in solid scientific reasoning. This makes the course a core component in preparing Master’s students to meet the demands of the job market and the global business environment.
- Enseignant: Ammar Hamioud