TecnoCampus Foundation
eCampus
University Business
eCampus

What are you looking for?

General information

Subject type: Mandatory

Coordinator: Jesus Ezequiel Martínez Marín

Trimester: Second term

Credits: 3

Teaching staff: 

Rosa Herrero Antón

Skills

Basic skills

  • CB6-Possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context

  • CB7. That students know how to apply the knowledge acquired and their ability to solve problems in new or little-known environments within broader (or multidisciplinary) contexts related to their area of ​​study. 

Specific skills

  • CE2. Apply tools and methodologies that facilitate creative and innovative thinking in everyday situations related to the supply chain environment and logistics and maritime businesses.

  • CE4. Strategically manage business innovation processes in the supply chain and the maritime business, from diagnosis to application, being able to align resources, capabilities and skills to implement them

  • CE7. Manage (plan, schedule and control) the flow of materials and information (supply chain flow) through the coordinated direction and management of the areas of purchasing, production and physical distribution of the company. 

Transversal competences

  • CT1. Show willingness to learn about new cultures, experiment with new methodologies and encourage international exchange in the context of logistics, supply chain and maritime business.

  • CT2. Demonstrate entrepreneurial leadership and leadership skills that build personal confidence and reduce risk aversion. 

  • CT3. Develop tasks applying the acquired knowledge with flexibility and creativity and adapting them to new contexts and situations. 

Description

Mathematical Models for Logistics. 

 

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

Model and solve logistical problems with uncertainty and risk 

Programming mathematical models in Logistics 

Working methodology

The subject uses the following work methodologies: 

Master class, case study, collaborative learning and problem solving. 

Contents

Topic 1: Introduction 

1.1 Concept of mathematical model 

1.2 Types of systems 

1.3 Methodologies and algorithms 

Topic 2: Graph theory 

2.1 Definition, representation and topology 

2.2 Examples of application 

2.3 Matrices associated with a graph and isomorphism of graphs 

2.4 Algorithms in graphs 

2.4.1 Minimum partial tree and Prim and Kruskal algorithms 

2.4.2 Shortest path and Dijkstra algorithm 

2.4.3 Flow problem in a network and the Ford-Fulkerson algorithm 

Topic 3: Stochastic processes 

3.1 Definition of stochastic processes and random variables 

3.2 Examples and special cases 

3.3 Discrete-time Markov chain 

3.4 Continuous time Markov chain 

Topic 4: Trade Traveler Problem 

4.1 Definition of the problem 

4.2 Most used variants 

4.3 Resolution methodologies 

Topic 5: Vehicle Route Problem 

5.1 Definition of the problem 

5.2 Most used variants 

5.3 Resolution methodologies 

Topic 6: Nonlinear programming 

6.1 Definition and qualification of nonlinearity 

6.2 Examples 

6.3 Special case: problems with linear constraints 

6.4 Karush-Kuhn-Tucker conditions and interpretation of Lagrange multipliers 

6.5 Resolution methodologies 

Learning activities

The subject uses the following work methodologies: 

Master class, case study, collaborative learning and problem solving. 

Evaluation system

In order to gather evidence of the achievement of the expected learning outcomes, the following evaluative activities will be carried out: 

  
A1. Exercise at home: Graf theory (10%) 

Exercises to be solved through the virtual classroom based on the theoretical content. 

  
A2. Exercise at home: Stochastic Processes (10%) 

Exercises to be solved through the virtual classroom based on the theoretical content. 

  
A3. Exercise at home: Business Traveler problem (15%) 

Exercises to be solved through the virtual classroom based on the theoretical content. 

  
A4. Exercise at home: Vehicle Route Problem (15%) 

Exercises to be solved through the virtual classroom based on the theoretical content. 

  
A5. Final exam (50%) 

Examination of the content of the whole subject. 

  
The mark of each student will be calculated following the corresponding percentages: 

Final grade = A1 0,1 + A2 0,1 + A3 0,15 + A4 0,15 + A5 0,5 

  
Considerations: 
- It is necessary to obtain a mark superior to 4 in the final examination to pass the asignatura. 

- The teacher will inform of the dates and format of the delivery of the exercises at home. An activity not delivered or delivered late and without justification (court summons or medical matter) counts as a 0. 

- It is the responsibility of the student to avoid plagiarism in all its forms. In the case of detecting a plagiarism, regardless of its scope, in some activity will correspond to have a mark of 0. In addition, the teacher will communicate the situation so that applicable measures are taken in the matter of sanctioning regime. 

REFERENCES

Basic

Hillier, FS, Lieberman, GJ (2016). Introduction to Operations Research. 10th. ed. McGraw-Hill Education.

Grassmann, WK, Tremblay, JP (2000). Logic and Discrete Mathematics. Prentice Hall.

Golden, BL, Raghavan, S., Wasil, EA (2008). The vehicle routing problem: latest advances and new challenges (Vol. 43). Springer Science & Business Media.

Gutin, G., Punnen, AP (2006). The traveling salesman problem and its variations (Vol. 12). Springer Science & Business Media.

Luenberger, DG, Ye, Y. (2015). Linear and Nonlinear Programming. 4th. ed. Springer.

Nelson, BL (2010). Stochastic modeling: analysis & simulation. Courier Corporation.

Taha, HA (2019). Operations Research: An Introduction. 10th ed. Pearson.

Complementary

Moreno S., Ma. Isabel, Sistachs V., Vivian, Díaz G., L. (2016). Selection of models in binary logistic regression, a classic approach. VDM Verlag.

Derbel, H., Jarboui, B., Siarry, P. (Eds.). (2020). Green Transportation and New Advances in Vehicle Routing Problems. 1st ed. Springer.