General information

Subject type: Mandatory

Coordinator: Jesus Ezequiel Martínez Marín

Trimester: Second term

Credits: 4

Teaching staff: 

María De Lourdes Eguren Martí

Teaching languages

  • Spanish
  • Catalan

Classes will be taught mainly in Spanish.


Specific skills
  • Show knowledge and skills for the coordination of the departments of purchasing, supply, production and distribution of a product to any company, analyzing different types of techniques

  • Select and use quantitative instruments for decision making and contrasting economic hypotheses


The subject "Quantitative Methods Applied to Logistics" aims to encourage and develop systemic and scientific thinking, allowing students to raise and develop models and solutions to problems of various kinds. 

More specifically, it aims to provide students with a series of tools and methods that allow them to solve problems in real life, and in the field of logistics in particular.


This subject has methodological and digital resources to make possible its continuity in non-contact mode in the case of being necessary for reasons related to the Covid-19. In this way, the achievement of the same knowledge and skills that are specified in this teaching plan will be ensured.

The TecnoCampus will make available to teachers and students the digital tools needed to carry out the course, as well as guides and recommendations that facilitate adaptation to the non-contact mode.

Learning outcomes

  •  Learn to use quantitative tools to model real logistical problems, applicable to the academic and professional field.
  • Learn to optimize these problems by means of exact and heuristic tools, considering the efficiency of the selected method according to the nature of the problem posed.
    • Learn to use a systemic approach to pose and solve problems, identifying key elements, available information, scope and ultimate goal

Working methodology

The teaching methodology of the subject is divided into two parts: face-to-face / online sessions, and independent work.

In the face-to-face sessions the teachers will teach the theory, interspersing practical examples that will facilitate the learning of the knowledge explained and will help the student in the resolution of exercises and practical activities.

In addition, the student will have to work autonomously both to delve into the theoretical and practical concepts seen in class and to do group work.


  1. Introduction
    1. Models: Concepts and typologies
    2. Systems: Concept, principles, and applications
    3. Methods: Concept, importance and utility.
    4. Algorithm: Concept, typology and use.
  2. Graph theory
    1. Introduction to graphs
      1. Definition, representation and topology
      2. Application examples
      3. Pseudocode: basic concepts, conditional operators and structure.
    2. Road problems
      1. Minimum partial tree
        1. Prim algorithm
        2. Kruskal algorithm
    3. Shorter path
      1. Dijkstra's algorithm
    4. Flow problems
      1. Maximum total flow
      2. Ford-Fulkerson algorithm
    5. Case studies
  3. Linear programming
    1. Introduction to linear programming
      1. What is linear programming?
      2. The first mathematical model
      3. Variable transformations
      4. Transformations of the objective function
      5. Constraints transformations
    2. Graphic resolution
      1. Feasible solutions area
      2. Basic and non-basic variables
      3. Optimal solution
      4. Types of solutions
    3. Dual model and sensitivity analysis
      1. Rules of primal-dual transformation
      2. Meaning of dual variables
      3. Sensitivity analysis of cost coefficients
      4. Sensitivity analysis of independent terms
      5. Dual price utility
    4. Whole and mixed linear programming
      1. Real, integer, and binary variables
      2. Usefulness of binary variables
    5. Case studies: Application to logistics
    6. Using the Excel "Solver" tool
  4. Heuristic algorithms applied to solving logistic problems
    1. Traveling Salesman Problem (TSP)
    2. Vehicle Routing Problem (VRP)
    3. Knapsack Problem (KP)
    4. Bin Packing Problem (BPP) Graph theory

Learning activities

The activities to be carried out by the student during the course are of various natures in accordance with what is specified in the methodology described.

In this sense and with the aim of covering the proposed goals, face-to-face activities will be carried out as well as in the virtual classroom. In addition, individual and group activities will be carried out.

Evaluation system

The overall grade of the subject takes into account the following aspects:


  • Exercises, practices and work (Not recoverable): 20%.
  • Group work (Not recoverable): 30%.
  • Final exam: 50%


To pass the course it is necessary to obtain at least a 4 in the final test.


recovery In the event that the subject is suspended, only the final test can be recovered. To access the recovery, it is necessary to have appeared in the final test



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Sallán JM, Suñé A, Fernández V, Fonollosa JB. Quantitative methods of industrial organization I. Edicions UPC (2nd ed.), 2005. ISBN: 8483017954.

Taha HA. Operations research. Editorial Pearson Education (7th ed.), 2004. ISBN: 9702604982.


Vieites Rodíguez, Ana María et al. Graph theory. Exercises and problems solved. Editorial Paraninfo, 2014. ISBN: 9788428337076