Curricular Unit: | Code: | ||

Quantitative Methods in Management | 987MTG | ||

Year: | Level: | Course: | Credits: |

2 | Undergraduate | Business Sciences | 6 ects |

Learning Period: | Language of Instruction: | Total Hours: | |

Winter Semester | Portuguese/English | 78 | |

Learning Outcomes of the Curricular Unit: | |||

To understand the importance of management science in decision-making in different fields, such as production, marketing, investment, scheduling, transportation or assignment. To represent a real system in a linear programming model; to solve the model; to obtain information from the model in order to support the decision-making process. To identify and address decision problems, using a structured approach; to build decision problem models; to identify and draw on analytical methods for obtaining solutions that support for informed decisions. | |||

Syllabus: | |||

1 Linear Programming 1.1 Mathematical formulation and properties of the model 1.2 Graphical solution 1.3 Excel/LINDO solution 1.4 Sensitivity analysis and duality 1.5 Special problems 1.5.1 Transportation problem (stepping stone algorithm) 1.5.2 Assignment problem (Hungarian algorithm) 2 Decision analysis 2.1 Decision-making without probabilities 2.2 Decision making with probabilities 2.2.1 Decision-making criteria 2.2.2 Expected value of perfect information 2.2.3 Decision trees 2.3 Decision analysis with additional information 2.4 Utility | |||

Demonstration of the Syllabus Coherence with the Curricular Unit's Objectives: | |||

1 Linear Programming. Allows representing a problem via a linear programming model, by formulating it as a mathematical model. Allows solving the model, using the graphical method and MS Excel Solver. The interpretation of the solution of the problem is accompanied by duality and sensitivity analysis to model parameters. Allows recognizing and solving transportation and assignment problems, by presenting its structure, formulation and resolution (stepping and stone method and Hungarian method, respectively), as well as the interpretation of the results. 2 Decision analysis. Allows recognizing and solving decision problems under uncertainty conditions relative to future events, as well as reporting the results for decision-making purposes. | |||

Teaching Methodologies (Including Evaluation): | |||

The course resorts to theoretical presentations, conceptual clarification and software. Lessons combine theory and practice, and case-study scenarios are analysed. Assessment is based on two midterm tests (40% + 40%) and a written report (20%). | |||

Demonstration of the Coherence between the Teaching Methodologies and the Learning Outcomes: | |||

The combination of theoretical presentations and problem-solving tasks enables students to acquire specific quantitative techniques in management knowledge, and to apply them to specific case-scenarios. To enable the analysis of realistic problems, which are typically high dimensional, the course uses software to do much of the computational and graphical work so students can focus on interpretation | |||

Reading: | |||

Hill, M. M. e Santos, M. M. (2015). Investigação Operacional, Volume I, Programação Linear, 3/e. Lisboa: Edições Sílabo. Hill, M. M., Santos, M. M. e Monteiro, A. L. (2015). Investigação Operacional, Volume 3, Transportes, Afetação e Otimização em Redes, 2/e. Lisboa: Edições Sílabo. Hillier, F. S. and Hillier, M. S. (2019). Introduction to Management Science: A Modelling and Case Studies Approach with Spreadsheets, 6/e. Boston: McGraw Hill. Lawrence, John A. and Pasternack, Barry Alan (2002). Applied Management Science: a computer-integrated approach for decision making, 2/e. New York : John Wiley & Sons . Taha, H. A. (2017). Operations Research: An Introduction, 10/e. Upper Saddle River, NJ: Prentice Hall. Taylor III, B. W. (2019). Introduction to Management Science, 13/e. New Jersey: Pearson. |