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Operations research

Code: 108241
Credits: 6
2026/2027
Degree programme Type Course
Aeronautical Management OB 3

Contact lecturer

Name :
Francisco Javier Delgado Vences
Email :
franciscojavier.delgado@uab.cat

Group languages

You can consult this information at the end of the document.

Prerequisites

Calculus-103815, and Linear Algebra-103814 .

Objectives

Operations Research (or Operational Research) consists in applying mathematical, statistical and algorithmical models to help decision-making. Problems involving making decisions arise in fields as diverse as business administration, industrial engineering, or economics and they all share the common goal of resource efficiency.


This course aims to provide basic knowledge of optimization models required to pose and solve common operational problems in Aeronautical Management, such as scheduling, fleet management, routing, etc... In addition, the student will become capable of dealing with a wider range of distribution, logistics, and transportation problems(air, marine or rail cargo, urban freight, supply chain management, etc...).


Learning outcomes

  • CM17 (Prepare opinions or reports based on optimisation models, simulation and quantitative techniques for decision-making processes, systematising, documenting and reflecting the process and mechanisms used.) Prepare opinions or reports based on optimisation models, simulation and quantitative techniques for decision-making processes, systematising, documenting and reflecting the process and mechanisms used.
  • CM18 (Propose decisions based on available and contrasted data and analysis tools in the context of the aeronautical industry that are in line with regulatory and ethical frameworks, and the objectives of the organisation. (CT04)) Propose decisions based on available and contrasted data and analysis tools in the context of the aeronautical industry that are in line with regulatory and ethical frameworks, and the objectives of the organisation. (CT04)
  • CM19 (Communicate data analysis results clearly and effectively to technical and non-technical audiences.) Communicate data analysis results clearly and effectively to technical and non-technical audiences.

Contents

Linear Programming.

Examples. Definitions.

The Simplex Method. Introduction.

The Simplex Method. Algorithm and tableau.

Duality and intro to Sensitivity Analysis.

Integer Programming.

Introduction.

The Branch and Bound Method.

Binary variables.

Linear Network Optimization.

Introduction and basic concepts.

The minimum-cost flow problem. The Network Simplex Algorithm.

The maximum flow problem. Ford-Fulkerson Algorithm.


Programming language for solving optimization problems.


Learning activities and methodology

Title Hours ECTS Learning outcomes
Problem resolution 13 0.52 CM17, CM18, CM19
Out-of-class/online activities 88 3.52 CM17, CM18, CM19
Computer lab 13 0.52 CM17, CM18, CM19
Theory 26 1.04 CM17, CM18, CM19


Classroom hours, within the context of guided learning activities, consist of:

Lectures:

The instructor explains the basic concepts of the subject, providing examples and applications, and tailored to the mathematical background of the class. Attendance and participation in the classroom are taken into account. Also, students are expected to devote some out-of-class time to homework and readings in order to complement lectures.

Problem Sets:

Through exercices, students will improve their understanding of the lecture material and practice some problem-solving techniques.

Computer Labs :

Students learn a programming language, and how to use it to formulate and solve course exercices with the help of a computer.

Transversal Skills

Peer collaboration in Problem-Set as well as in Computer-Lab meetings, where joint discussion of course exercises takes place, helps to improve cooperative learning and teamwork skills(T03). The instructor provides the class with tools for both analysis and synthesis, which contribute to the enhancing of thinking habits (T01), communication, and individual creativity(T04,T06). The grading of the computer-lab and problem-set assignments should thus reflect the student's ability to communicate and engage in teamwork, in addition to personal attitude and study habits.

Teaching will be offered on campus or in an on-campus and remote hybrid format depending on the number of students per group and the size of the rooms at 50% capacity.


AI

En aquesta assignatura, es permet l'ús de tecnologies d'Intel·ligència Artificial (IA) com

a part integrant del desenvolupament del treball, sempre que el resultat final reflecteixi una contribució

significativa de l'estudiant en l'anàlisi i la reflexió personal. L'estudiant haurà d'identificar clarament quines

parts han estat generades amb aquesta tecnologia, especificar les eines emprades i incloure una reflexió crítica

sobre com aquestes han influït en el procés i el resultat final de l’activitat. La no transparència de l’ús de la IA

es considerarà falta d'honestedat acadèmica i pot comportar una penalització en la nota de l'activitat, o

sancions majors en casos de gravetat

Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.


Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Exams 80% 4 0.16 CM17, CM18, CM19
Computer-Lab assignments 20% 6 0.24 CM17, CM18, CM19


This subject does not offer a unique evaluation option

Course grade will be calculated as the weighted average of the scores obtained in the graded tasks. The percentage breakdown is the following:

Computer-Lab Assignments: 20% (3 assignments)

Midterm Exam: 30% (1 exam)

Final Exam: 50% (1 exam)

Exam Retake Option: There is only one possible retake for the two exams (80%). To be eligible to retake the final exam, you should have participated in graded tasks adding up to a minimum percentage of 70%.

If you fail the course, your grade will be the lowest value between 4.5 and the weighted average of all graded-task scores.

Graded-task schedule:

Graded-task dates(assingments and midterm exams) will be announced on Virtual Campus UAB and may be subject to change. Always log into Virtual Campus to check for schedule changes. Virtual Campus is the standard information sharing environment among students and teaching staff.

School policy on cheating :

Any dishonest behavior in order to get a grade higher than your own work merits will be considered cheating and will receive a zero score on the assignment or exam on which you cheated. Therefore, copying anothers's test, or allowing others to copy your work will result in failing the task with a zero score. Neither a make-up assignment nor a retake exam will be granted, and this will lead to failing the whole course.



Bibliography

Course material

Alabert, Aureli; Curs d'investigació Operativa. Apunts. (http://mat.uab.cat/alabert/Docs/teaching/Optimisation.pdf )

Fourer, R., Gay, D.M. & Kernighan, B.W.; AMPL. A Modeling Language for Mathematical Programming. Pacific Grove: Thomson/Brooks/Cole, cop. 2003. (https://ampl.com/resources/the-ampl-book)

Suggested textbooks

Basart, Josep M.; Programació Lineal. Materials UAB 58, 2000.

Taha, Hamdy A.; Operations Research. Pearson Education, 8th. ed., 2007.

Additional readings

Bazargan, Massoud; Airline Operations and Scheduling. Ashgate, 2004.

Pujolar, David; Fundamentos de programación lineal y optimización de redes. Materials UAB 146, 2004.


Software


Solve of Excel

AMPL

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 11 Catalan/Spanish first semester afternoon
(PAUL) Classroom practices 11 Catalan/Spanish first semester afternoon
(PAUL) Classroom practices 12 Catalan first semester afternoon
(PLAB) Practical laboratories 21 Catalan first semester afternoon
(PLAB) Practical laboratories 22 Catalan first semester afternoon
(PLAB) Practical laboratories 23 Catalan first semester afternoon