
Quantitative Methods for Decision Making
Code: 108243Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Aeronautical Management | OP | 4 |
Contact lecturer
- Name :
- Juan José Ramos Gonzalez
- Email :
- juanjose.ramos@uab.cat
Teaching staff
- Zhiqiang Liu
Group languages
You can consult this information at the end of the document.
Prerequisites
It is recommended to have successfully completed the following courses:
- Optimitzation;
- System Modeling and Simulation
- Advanced Informatics
Objectives
Modeling and simulation, as well as operation research, become a supportint tool in the decision-making processes to improve logistics processes. The main objective of the course is to deepen some quantitative methods that help improve the decision-making processes in the context of the management of operations in air transport. For example, airlines have used operational research techniques since the 1950s in the planning and management of their operations. Based on mathematical programming, the use of Constraint Logic Programming (CLP) is presented to solve problems in decision making or optimization. The guidelines for using CLP for different types of problems will be given with the following objectives:
- Characterize the available resources and the expected demand.
- To properly identify the decision variables and their domains.
- Form the problem restrictions
- Identify and program the method of solving feasibility and optimization problems.
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.
- KM36 (Identify the main quantitative methods and techniques to support decision-making in aeronautical management.) Identify the main quantitative methods and techniques to support decision-making in aeronautical management.
- SM27 (Apply appropriate mechanisms to identify and solve decision problems in logistics and management systems in the aeronautical sector, using qualitative and quantitative methods to analyse alternative solutions, evaluate their feasibility, select the best option, and optimise decisions through strategic analysis and analytical methodologies. (ST10)) Apply appropriate mechanisms to identify and solve decision problems in logistics and management systems in the aeronautical sector, using qualitative and quantitative methods to analyse alternative solutions, evaluate their feasibility, select the best option, and optimise decisions through strategic analysis and analytical methodologies. (ST10)
Contents
Theory and Problems
MQL.T.1. Introduction to Decision Making:
- DM in LSCM:
- SCM Modeling
- Advanced Planning
- Quantitative Methods
- Planning and Scheduling
- Forecasting
MQL.P.1. Examples:
- Demand Forecast
- Production Mix
MQL.T.2. Planning and Scheduling. Optimization Methods:
- Mixed Integer Programming
- Constraint Programming
- AI methods
MQL.P.2. Introductory exercises to optimization problem modeling
MQL.T.3. Production Planning:
- Planning goals and activities
- Planificació dela producció
- Modelatge de les restriccions
MQL.P.3. Production Planning models. Optimization exercises.
MQL.T.4. Production scheduling:
- Advanced modeling constraints
- Programació d'activitats i objectius
MQL.P.4. Production Scheduling models. Optimization exercises.
MQL.T.5.Transport operations planning:
- Supply and Transport Networks
- Airline Operations
- Fleet Assginment Models
- Aircraft Routing
MQL.P.5. Exemples de problemes d'optimització en el transport aeri
Pratice sessions
MQL.L.1. Introduction to OPL:
- S/W configuration
- IDE introduction
MQL.L.2. OPL models:
- Mathematical Programming
- Constraint programming
MQL.L.3. Production Planning and Scheduling
MQL.L.4. Transport Network Models
MQL.L.5. Airline Operations
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Theory sessions | 18 | 0.72 | |
| Problems sessions | 12 | 0.48 | |
| Personal study | 30 | 1.2 | |
| Practise sessions | 10 | 0.4 | |
| Development of practical work | 48 | 1.92 | |
| Practice exercise preparation | 20 | 0.8 | |
| Practical work followup sessions | 8 | 0.32 |
The general methodological approach of the subject is based on the principle of multiple strategies, so it is intended to facilitate active participation and the construction of the learning process by the student. For this purpose, teaching activities will be organized as magisterial sessions with the whole group, and practical sessions and follow-up of the student work with reduced groups.
Teaching will be offered on campus.
Specifically, the teaching activities included in this subject are the following:
- Theory lectures: Presentation and discussion of the fundamental concepts of the subject (whole group).
- Problem sessions: Resolution and discussion of exercises aimed to consolidate the theoretical concepts of the subject (whole group).
- Practise sessions. Basically, introductory sessions will be held to:
- OPL mathematical programming environment.
- Constraint Programming Library of ILOG CP.
These activities will be carried out in small groups. It is recommended to attend all the practical sessions in order to be able to pass the validation exam of the practical part.
Use of AI
This subject recognises the growing use of generative artificial intelligence as a support tool, and therefore its use is admitted in a limited way. To all intents and purposes, the use of these tools will only be accepted to improve formal aspects of papers, such as writing, style, expository clarity, linguistic correctness or translation, and to obtain occasional assistance in technical aspects. It is not acceptable to use generative artificial intelligence tools to generate the content of assessed work, such as methodological approaches, designs, experiments, analysis or interpretation of results, elaboration of ideas, or formulation of conclusions. These tasks must be carried out entirely by the student, as they constitute the essential part of the intellectual and creative work required to pass the subject. Students will have to explicitly indicate, in each of the deliverables, whether generative artificial intelligence tools have been used, specifying which ones have been used, for what purpose and to what degree. Irresponsible, excessive or unnecessary use of these tools may have a negative impact on the final grade of the subject. The detection of undeclared or inappropriate use of these tools may lead to failure of the subject.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Practical Exercises | 40% | 0 | 0 | CM17, CM18, CM19, KM36, SM27 |
| Theory part exam | 40% | 2 | 0.08 | CM18, KM36, SM27 |
| Practical validation test | 20% | 2 | 0.08 | KM36, SM27 |
The single assessment system is not foreseen in this subject.
a) Scheduled evaluation process and activities
The evaluation consists of the following activities:
- Submission of practical exercises (40%): A set of individual practical exercises to be submitted on the specified dates.
- Examination validating the practical component (20%): Intended to assess whether the student has acquired sufficient knowledge of the optimization tools introduced in the course.
- Theory examination (40%): An examination covering the theoretical content of the course.
Please note that the practical component is not recoverable. Therefore, obtaining a grade below 4 out of 10 in this component will preclude passing the course. Any practical assignment not submitted by the established deadline will not be assessed (except in duly justified cases, in which the maximum grade will be 5). To be eligible for assessment, all practical assignments must be submitted.
b) Programming evaluation activities
The schedule for regular evaluation activities will be published on the Virtual Campus at the beginning of the semester. Retake dates will be announced in the examination section of the School of Engineering website.
c) Retaking process
According to the UAB Academic Regulations, to be eligible for the resit of a failed assessment, the student must have previously been assessed in activities whose combined weight is at least two thirds of the total grade for the course or module.
Practice work can't be retaken and must be submitted within the specified due dates.
d) Procedure to review qualifications
For each evaluation activity, a date, time, and place will be specified for students to review their work with the instructor. The faculty member responsible for the course will consider any appeals regarding the assigned grade. Appeals must be submitted on the specified date; no reviews will be conducted thereafter.
e) Qualifications
The final grade is calculated as follows:
FINAL mark = CE1 x 0,4 + CE2 x 0,2 + CE3 x 0,4
CE1: Grade for the practicals. Each practical averages the same weight in the calculation of CE1. CE1 must be greater or equal to 4
CE2: Examination validation practical part. CE2 must be greater or equal to 4
CE3: Theory exam. CE2 must be greater or equal to 4
If any evaluation component (CE1 or CE3) is below 4, or CE2 is below 5, the final grade will be recorded as Fail (4), regardless of the result obtained from the grading formula.
Awarding an honours mention (MH) is the decision of the lecturers responsible for the subject. UAB regulations state that MH may only be awarded to students who have obtained a final grade of 9.00 or higher. Up to 5% of the total number of students enrolled may be awarded MH
A grade of Not Assessable (Not Submitted) will only be assigned if no assessable work is submitted during the course..
f) Irregularities by the student, copy and plagiarism
Without prejudice to other disciplinary measures deemed appropriate, and in accordance with current academic regulations, any irregularity committed by the student, which could lead to an alteration of the evaluation act, will be scored with a zero. Therefore, copying or allowing to copy a practice or any other activity spoiling the evaluation will imply failing with a zero, and if the activity is required to pass the subject, the whole course will be failed. The evaluation activities qualified in this way and by this procedure will not be recoverable, and therefore the subject will be failed directly without the opportunity to retaking it in the same academic year.
h) Evaluation of students retaking the whole subject
Those students retaking the whole subject will do the same assessment activities.
Bibliography
- Hartmurt Stadlert and Cristoph Kilger (Eds.) Supply Chain Management and Advanced Planning. Third Edition. Springer, 2005. (Electronic version available at the university library) https://bibcercador.uab.cat/permalink/34CSUC_UAB/1eqfv2p/alma991010559602806709
- Ioannis T. Christou. Quantitative Methods in Supply Chain Management. Models and Algorithms. Springer, 2012. (Electronic version available at the university library) https://bibcercador.uab.cat/permalink/34CSUC_UAB/1eqfv2p/alma991010405513506709
- H. Paul Williams. Model Building in Mathematical Programming. Wiley. 2013 (5th edition).https://bibcercador.uab.cat/permalink/34CSUC_UAB/1c3utr0/cdi_askewsholts_vlebooks_9781118506189
- Kim Marriott and Peyer J. Stuckey. Programming with Constraints. An introduction. MIT Press.
- Massoud Bazargan. Airline Operations and Scheduling. Ashgate. https://ebookcentral-proquest-com.are.uab.cat/lib/uab/detail.action?docID=5208383
- Norman Ashford et Al. Airport Operations. McGraw-Hill
Further readings
- Joseph Geunes, Panos M. Pardalos and H. Edwin Romeijn (Eds.) Supply Chain Management: Models, Applications, and Research Directions. Kluwer Academic Publishers, 2002. (Electronic version available at the university library) https://bibcercador.uab.cat/permalink/34CSUC_UAB/1eqfv2p/alma991010403666106709
- F. Robert Jacobs, William L. Berry, D. Clay Waybark and Thomas E. Vollmann. Manufacturing Planning and Control for Supply Chain Management. McGraw-Hill, 2011 (6th edition)
- F. Robert Jacobs and Richard B. Chase. Operations and Supply Chain management. McGraw-Hill Irwing, 2011 (13 th edition)
Software
Specific software
During the course we will use the IBM ILOG optimization platform that you can install on your computers.
How to get the ILOG Student Edition platform.
When starting the course go to: https://www.ibm.com/products/ilog-cplex-optimization-studio?mhsrc=ibmsearch_a&mhq=ilog
Register on the platform with your email address @ e-campus.uab.cat
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 | 1 | Catalan/Spanish | first semester | afternoon |
| (PAUL) Classroom practices | 1 | Catalan/Spanish | first semester | afternoon |
| (PLAB) Practical laboratories | 1 | English | first semester | afternoon |
| (PLAB) Practical laboratories | 2 | English | first semester | afternoon |