Universidad Pública de Navarra - Nafarroako Univertsitate Publikoa
Public University of Navarre
| Course code: 720308 | Subject title: Industrial Optimization | ||||
| Credits: 3 | Type of subject: Mandatory | Year: 1 | Period: 1º S | ||
| Department: Estadística, Informática y Matemáticas | |||||
| Lecturers: | |||||
| AZCARATE CAMIO, CRISTINA (Resp) [Mentoring ] | FAULIN FAJARDO, FCO. JAVIER [Mentoring ] | ||||
| SERRANO HERNANDEZ, ADRIAN [Mentoring ] | |||||
Contents
Linear optimization. Integer optimization. Multi-objective optimization. Optimization Software. Applications in industrial engineering. Discussion of real cases.
General proficiencies
CB7: That students know how to apply the acquired knowledge and their ability to solve problems in new or little-known settings within broader (or multidisciplinary) contexts related to their area of study.
CB9: That students know how to communicate their conclusions and the latest knowledge and reasons that support them to specialized and non-specialized audiences in a clear and unambiguous way.
CB10: That students possess the learning skills that allow them to continue studying in a way that will have to be largely self-directed or autonomous.
CG1: Have adequate knowledge of the scientific and technological aspects of: mathematical, analytical and numerical methods in engineering, electrical engineering, energy engineering, chemical engineering, mechanical engineering, continuous media mechanics, industrial electronics, automation, manufacturing, materials, methods quantitative management, industrial computing, urban planning, infrastructure, etc.
CG8: Apply the acquired knowledge and solve problems in new or little-known environments within broader and multidisciplinary contexts.
Specific proficiencies
CMG5: Knowledge of management information systems, industrial organization, production and logistics systems, and quality management systems.
CMG6: Capabilities for work organization and human resource management.
CMG9: Ability to manage Research, Development and Technological Innovation.
Learning outcomes
R1. Knowledge of the fundamentals of linear optimization, integer optimization and multi-objective optimization.
R2. Ability to identify optimization problems in the context of industrial engineering.
R3. Ability to represent real problems using a linear, integer or multi-objective optimization model, to solve it using the appropriate software, and to collect, analyze and interpret its results.
Methodology
| Training activity | Hours | %Teaching at classroom (face-to-face or virtual) |
| AF1.- Theoretical Classes | 15 | 100 |
| AF2.- Practical Classes | 12 | 100 |
| AF3.- Assignments and Projects in Groups | 13 | 0 |
| AF4.- Personal study and autonomous work | 30 | 0 |
| AF5.-Instructor meetings and assessment proofs | 5 | 100 |
Evaluation
| Learning outcome |
Assessment activity |
Weight (%) | It allows test resit |
Minimum required grade |
|---|---|---|---|---|
| R1 | Short tests in continous assessment | 20 | NO | 5 out of 10 |
| R1,R2,R3 | Exams | 60 | YES | 5 out of 10 |
| R1,R2,R3 | Assignments and projects | 20 | NO | 5 out of 10 |
Agenda
Chapter 1: Linear Optimization
1.1 Problems formulation of linear optimization.
1.2 Mathematical background of linear optimization.
1.3 Simplex algorithm
1.4 Other procedures for linear optimization
1.5 Duality theory and sensitivity analysis
1.6 Linear Optimization Software
Chapter 2: Integer Linear Programming.
2.1 Problems formulation of integer linear programming.
2.2 Solving procedures: Branch and Bound algorithm.
2.3 Other solving techniques.
2.4 Integer Linear Optimization Software.
Chapter 3: Multicriteria Optimization
3.1 Problems formulation of multicriteria optimization.
3.2 Efficient Solution and efficient set.
3.3 Solving procedures: generating methods and goal programming.
3.4 Other solving techniques
Chapter 4: Industrial Engineering Applications. Discussion of real cases.
4.1 Analysis of real cases.
4.2 Reading of research papers.
Bibliography
Access the bibliography that your professor has requested from the Library.
Main References
HILLIER, F.S., LIEBERMAN, G.J. (2021): Introduction to Operations Research. McGraw Hill, 11e
Complementary References
ANDERSON, D. R., SWEENEY, D. J., WILLIAMS, T. A., CAMM, J., FRY, M. OHLMANN,J.W, (2016): Introduction to Management Science. Quantitative Methods for Decision Making. Thomson. Cincinnati, USA. 14e
BAZARAA, MS., JARVIS, J.J., SHERALI, H.D. (2010): Linear Programming and Network Flows. Wiley, 4e.
COLLIER, D.A., EVANS, J.R. (2020): Operations and Supply Chain Management. Cengage, 2e.
HILLIER, F.S., HILLIER, M.S. (2010): Introduction to Management Science. A Modeling and Case Studies Approach with Spreadsheets. McGraw-Hill 4e
LAWRENCE, A.L., PASTERNACK, B.A. (2002): Applied Management Science. Modeling, spreadsheet analysis and communication for decision-making. Wiley, 2e
RUSELL, R.S., TAYLOR, B.W. (2016): Operations and Supply Chain Management. Wiley, 9e
WINSTON, W.L. (2005): Operations Research. Applications and Algorithms. Thomson, 4e
WINSTON, W.L., ALBRIGHT, S.L. (2016): Practical management science. South-Western Cengage Learning, 6e
Research Journals: Optimization and Engineering, INFORMS Journal of Applied Analytics, International Transactions on Operational Research, Omega, European Journal of Industrial Engineering, European Journal of Operational Research, Computers and Industrial Engineering, etc.