Degree | Type | Year | Semester |
---|---|---|---|
4313785 Aeronautical Management | OB | 0 | 2 |
Subjects M4, M5 and M6
Resources Management in a dynamic context characterized by constant changes due to both the uncertainty in the duration of activities and the constant disturbances that flows among processes is considered as a complex system due to the interdependencies between activities and resources, which affect the performance of the system as a whole.
A holistic approach that allows obtaining a better knowledge of the different emerging dynamics that usually appear in systems with a high degree of interdependencies, is essential to be able to improve the performance of the system and design mitigation mechanisms for the propagation of disturbances between the different processes.
The main objective of this module is to consolidate the academic concepts introduced in the M4 module of decision making in the resolution of complex problems described in modules M5 and M6. Students will be introduced to a causal approach in the development of simulation models, which will allow them to acquire a better knowledge of the effects of uncertainties on the overall behavior of the system. The causal models will be formalized through Colored Petri Nets to describe the cause-effect relationships between the activities and the airport resources. To achieve the present training objective, the following sub-objectives are considered:
Introduction to holistic analysis, as opposed to a reductionist approach, in which the airport system is modeled as a set of resources that interact in a dynamic context.
Identify the interactions between the resources and the activities to be carried out, which determine the behavior of the airport system.
Identify emerging dynamics as the result of cause-effect relationships.
Development of simulation models considering the variables of influence.
Theory
GRA.T.1: Introduction to Resource Management in a dynamic context
Flexibility as a source of complex problems
Performance Indicators
GRA.T.2: Modeling of Discrete Event Systems
Definition and Concepts.
Petri Nets: Specification of logical relationships.
Colored Petri Nets: Information flow specification.
GRA.T.3: State Space
The reachability tree
Analysis of causal relationships
Mechanisms of mitigation of undesirable dynamics.
GRA.T.4: Causal Simulation Models
Test-and-error approach
Validation and Verification of Simulation Models.
GRA.T.5: Experimental approach to minimize operations without added value:
Evaluation of bottlenecks
Policies based on Little's Law
Algorithms of minimization of variance
PROBLEMS
GRA.P.1 Examples:
Simulation of passenger flow throught the screening process
Simulation of arrivals and departures in a shared mode runway
Gate assignment model
GRA.P.2 Petri Nets Exercises
GRA.P.3 Colored Petri Nets Exercises
GRA.P.4 Exercises in CPN-Tools
GRA.P.5 State Space Exercises
Lab Exercises
GRA.L.4 Introduction to SIMIO
GRA.L.5 Flows in SIMIO
GRA.L.6 Simulation Project
The course is organized through master classes. The learning process will combine the following activities:
Theoretical lectures
Problem Sessions
Practical exercises: simulation laboratory, group work and oral presentations.
Autonomous work.
Practical case studies and simulation tools are used to improve the experience of students in the management of airport resources.
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.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Master Lectures | 20 | 0.8 | 1, 7, 5, 3, 9 |
Problem Exercises | 10 | 0.4 | 7, 5, 4, 8 |
Type: Supervised | |||
Lab Exercises | 15 | 0.6 | 7, 5, 6, 8, 2 |
Type: Autonomous | |||
Home Work | 34 | 1.36 | 5, 3, 4, 9 |
Modeling | 70 | 2.8 | 7, 5, 6, 8 |
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Petri Net Models | 20% | 0 | 0 | 1, 7, 5, 3, 4, 8 |
Simulation Models | 30% | 0 | 0 | 1, 7, 4, 6, 2, 9 |
State Space and Defense | 50% | 1 | 0.04 | 1, 5, 3, 4, 8, 2 |
N.Viswanadham,Y. Narahari. Performance Modeling of Automated Manufacturing Systems. Prentice Hall. 1992.
Merkuryev, Merkureva, Guasch, Piera: Simulation-Based Case Studies in Logistics: Education and Applied Research. Springer London. 2009.
Guasch, Piera, Casanova, Figueras: Modelado y Simulación : Aplicación a procesos logísticos de fabricación y servicios. Ed. UPC. 2002.
Lecturas Adicionales
Javier Campos, Carla Seatzu, Xiaolan Xi. Formal Methods in Manufacturing. CRC Press 2014.
Taylor. Agent Based Modeling and Simulation. Palgrave Macmillan. 2014
N. Gilbert . Simulation for the Social Scientist.. Open University Press.
CPN-Tools (https://cpntools.org/)
SIMIO