Quantitative pricing and revenue optimization - Dr. Reinhard Bürgy - Fall semester
Pricing and revenue optimization, also called revenue management, is a quantitative approach to setting and updating pricing and product availability decisions in a consistent and effective fashion. This approach has proven particularly successful in the airline industry, where fares and ticket offerings dynamically change as a function of the number of free seats, forecast of future demand and specific request characteristics.
Fostered by the development of online business and big data technologies, the adoption of revenue management has not only changed the airline sector but disruptively transformed the transportation, hospitality and advertising industries. In fact, revenue management is becoming increasingly important in a broad range of sectors including finance, retail and manufacturing.
Through a mix of lectures, case studies and guest speakers, we thoroughly discuss tactical decisions related to pricing and capacity allocation faced by companies that have some power to divide their customers into segments and to charge different prices to each segment. We first introduce the basics on pricing with and without a capacity (also called supply) constraint, then discuss price differentiation aspects and look at revenue optimization problems including customer segmentation. From these single resource problems, we move to the network case, in which multiple resources are used to provide a service. We also address overbooking aspects for dealing with no-shows and cancellations, and discuss markdown management for clearance of the inventory.
We typically use (relatively simple) quantitative models to address the revenue optimization problems under study. We therefore assume that the students have some basic knowledge of mathematical modeling and optimization including how to mathematically describe an optimization problem, how to implement it in a spreadsheet, how to get a solution, and how to interpret it.
Link to the course.
Graph Theory and Applications - Dr. David Schindl - Fall semester
In this course, we first introduce some basic concepts and notions of graph theory. We then present a series of graph theoretical problems (vertex coloring, edge coloring, maximum matching, …) which have real world applications (in sports scheduling, timetabling, transmission problems, … ) and focus on how these problems may be solved. The students will also learn how to model other real world problems using the graph theoretical notions introduced. With this course, the students will get familiar with the basic notions and fundamental problems in graph theory. They will learn how to use these theoretical problems to model real world problems as well as how to solve them.
Link to the course.
Supply Chain Management & Logistics - Prof. Marino Widmer - Fall semester
This course considers the various aspects of the supply chain management (SCM), which lets an organization get the right goods and services to the place they are needed at the right time, in the proper quantity and at an acceptable cost. Efficiently managing this process involves overseeing relationships with suppliers and customers, controlling inventory, forecasting demand and getting constant feedback on what is happening at every link in the chain. To reach these objectives, this course will be composed of the following parts:
- an introduction to the integrated production management (IPM) and an analysis of each module of the IPM
- supply chain management: strategic level
- supply chain management: operational level
Lien vers le cours.
Advanced Topics in Decision Support - Prof. Bernard Ries - Spring semester
In this course, we start with a presentation of the basic notions in decision theory. We focus on several existing decision criteria and analyse their weaknesses and strengths. We particularly focus on decision criteria under probabilistic uncertainty and o how to handle additional information in a decision process. We then present an introduction to utility theory, explain how to use utility functions and also how to construct such functions using loteries.
In a second part, we will concentrate on network optimization problems (max flow, min cost flow, etc...). Our focus will be on the modeling part but we will also see how to solve such problems efficiently.
Finally, we give a short introduction to multi-criteria decision theory and present some existing methods (ELECTRE, AHP).
All topics will be illustrated by examples taken from economics, management science and operations research.
Link to the course.
Advanced mathematical modeling and optimization - Dr. Reinhard Bürgy - Spring semester
This course considers modeling and optimization aspects of mixed-integer linear programming (or integer programming for short). This important subdomain of mathematical programming and extension of linear programming considers the problem of optimizing a linear function of many variables, some or all of them restricted to be integers, subject to linear constraints.
Integer programming is a thriving area of optimization. It has countless applications in production planning and scheduling, logistics, layout planning and revenue management, to name just a few. Thanks to effective and reliable software, it is widely applied in industry to improve decision-making.
In this course, we cover the theory and practice of integer programming. In the first part, we address mathematical modeling aspects. We discuss how integer variables can be used to model various practically relevant, complex decision problems. We then introduce some standard optimization problems and develop, analyze and compare different integer programming formulations for them. We also introduce powerful modeling and solving tools and test them on the optimization problems given in the course. In the second part, we address optimization aspects, in which we discuss the basic methodology applied to solve integer programs. In particular, we consider implicit enumeration techniques (branch and bound), polyhedral theory, cutting planes and primal heuristics. We also look at some advanced techniques, such as Danzig-Wolfe decomposition and column generation.
With this course, the students gain the ability to formulate and solve practically relevant decision problems using integer programming, and they understand the basic methodology for solving integer programs and its implications with respect to modeling decisions.
This course is designed for information systems, computer science and management student who have a good understanding of modeling and solving linear programs (as taught in the course Decision Support I).
Conforti, Michele, Gérard Cornuéjols, and Giacomo Zambelli. Integer programming, Graduate Texts in Mathematics. Springer (2014).
Link to the course.