Institution: Graduate School der Fakultät Wirtschafts- und Sozialwissenschaften der Universität Hamburg

Studiengang: Promotionsstudiengang

Dozent/in: Prof. Dr. Wolfgang Brüggemann

Termin(e): 1. Hälfte wöchentlich; 2. Hälfte Blockveranstaltung

Lehrveranstaltungsart: 1. Hälfte Vorlesung; 2. Hälfte seminarähnliche Projektarbeit

Raum:

Semester-Wochen-Stunden:

Unterrichtssprache: Deutsch und Englisch

Zielgruppe: BWL mit Interesse an der numerischen Lösung von Optimierungsproblemen

Beschreibung:

Qualifikationsziele: Die Teilnehmer sollen am Ende des Kurses die in ihrer eigenen Forschungsarbeit auftretenden Probleme fehlerfrei mit den Methoden der Komplexitätstheorie analysieren können und ggf. Unterschiede hinsichtlich der Eignung verschiedener Modellierungsansätze aus dieser theoretischen Perspektive erkennen können und als Motivation zur Konzeption neuer Ansätze nutzen.

Inhalte:

• Grundideen der Komplexitätstheorie
• Klassische Anwendungen im O&SCM-Bereich
• Analyse typischer Fehler bei der modellbasierten Untersuchung der Komplexität von wirtschaftswissenschaftlichen Problemen
• Minimale Modelle
• Anwendung auf aktuelle Probleme

Voraussetzungen für die Teilnahme: Idealerweise bringen die Teilnehmerinnen und Teilnehmer (gemischt-ganzzahlige) Optimierungsprobleme aus ihrer eigenen Forschungsarbeit mit, die sie auf ihre Komplexität untersuchen wollen oder auch schon untersucht haben. Vorkenntnisse im Umfang etwa aus der BWL-Master-Vorlesung „Vertiefungen zum Operations Research“ sind hilfreich, aber nicht zwingend erforderlich.

Art, Voraussetzungen und Sprache der Modulprüfung: Die Modulprüfung besteht aus einem schriftlichen Bericht über die Analyse der Komplexität eines eigenen (oder vom Veranstalter vergebenen) Problems

Anmeldung über die Graduate School der Fakultät Wirtschafts- und Sozialwissenschaften der Universität Hamburg (Dr. Ulf Beckmann)

Institution: Graduate School der Fakultät Wirtschafts- und Sozialwissenschaften der Universität Hamburg

Studiengang: Promotionsstudiengang

Dozent/in: Stefan Voß

Termin(e):
3-Tage-Block ca. Ende Nov./Anfang Dez. 2012
Plus Metaheuristics-Tagung am 28.02.-01.03.2013

Lehrveranstaltungsart: Workshop

Raum: n.a.

Semester-Wochen-Stunden: 2

Unterrichtssprache: englisch

Zielgruppe: Whoever is interested. PhD or advanced master students in business administration, economics, information systems, mathematics, civil engineering and alike.

Beschreibung:
Metaheuristics support managers in decision making with robust tools providing high quality solutions to important problems in business, engineering, economics and science in reasonable time horizons. While finding exact solutions in these applications still poses a real challenge despite the impact of recent advances in computer technology and the great interactions between computer science, management science / operations research and mathematics, (meta-) heuristics still seem to be the methods of choice in many (not to say most) applications. In this course we give some insight into the state of the art of metaheuristics. This focuses on the significant progress regarding the methods themselves as well as the advances regarding their interplay and hybridization with exact methods.

Basically, a metaheuristic is a top-level strategy that guides an underlying heuristic solving a given problem. That is, a metaheuristic is an iterative master process that guides and modifies the operations of subordinate heuristics to efficiently produce high-quality solutions. It may manipulate iteratively a complete (or incomplete) single solution or a collection of solutions. The subordinate heuristics are, e.g. high- (or low-) level procedures, simple local search, or just a construction method. Metaheuristics may use learning strategies to structure information in order to find optimal or near-optimal solutions efficiently.

Assuming a given problem, the goal is to find an optimal or at least a high-quality solution.

Each problem is associated with a solution space containing all feasible solutions according to the restrictions of the problem. One way of finding an optimal solution could be the search through all solutions and selecting the best one. Unfortunately, the size of the solution space is too large to accomplish all comparisons within a realistic time span and, therefore, heuristic methods have to be applied that limit the search on interesting areas of the solution space.

Well-known examples of metaheuristics are, e.g., simulated annealing, genetic algorithms, ant systems and tabu search.

In this course we learn about the basics of metaheuristics (What are metaheuristics? Why do we need them? What are the basic ingredients? How could we implement them? How could they be applied in different settings? etc.). Moreover, we are going to discuss the use of metaheuristics in different problem domains, let it be in computational logistics, telecommunications, production planning, marketing plus other domains the students might be interested in. Interest given, we could extend into hybridization with other types of methods like simulation, mathematical programming and alike.

Anmeldung über die Graduate School der Fakultät Wirtschafts- und Sozialwissenschaften der Universität Hamburg (Dr. Ulf Beckmann)

Institution: Graduate School der Fakultät Wirtschafts- und Sozialwissenschaften der Universität Hamburg

Studiengang: Promotionsstudiengang

Dozent/in: Thorsten Teichert

Termin(e):
25.11.2012, 11.00 – 19.00 Uhr
26.11.2012, 10.00 – 18.00 Uhr

Lehrveranstaltungsart: Workshop

Raum: n.a.

Semester-Wochen-Stunden: 1

Unterrichtssprache: englisch

Zielgruppe: BWL/VWL/Sozialwissenschaften/Sozialökonomie

Beschreibung:

Seminar Objectives:

Discrete choice experiments are a research method which is widely applied in the areas of management, consumer research and economic evaluation to estimate metric preference functions from choice input data. The goal of this research workshop is to give insights into newest developments of this method. Topics covered include: A tutorial on traditional conjoint – Choice context effects – Latent class – Hierarchical Bayes – Best-Worst Scaling

The workshop is oriented towards the exchange of research ideas, advanced concepts as well as preliminary and final research findings. There will be time for open discussion related to your research work.

Cornerstones:

DAY 1:
A tutorial on conjoint experiments: We will trace the versions of conjoint from its analytic roots to choice-based conjoint that is the standard in business. We will show how to use project individual-level results to a population using simulators.

Experimental Design techniques: Deriving designs from orthogonal arrays is an elegant way to generate conjoint design. You will learn how to quickly easily generate a design that is, if not optimal, robustly good for a particular application.

DAY 2:
Segmentation and Latent class: Latent class models are a finite mixture application for discrete choice experiments. They account for heterogeneity by positing a number of classes of “latent” respondents reflecting groups of people with similar values same way.

Hierarchical Bayes: Hierarchical Bayes has revolutionized the assessment of value functions. We examine why it works and explore other applications of Bayesian estimation in preference assessment.

Context effects: We examine the lessons from research from the last 20 years on the asymmetric dominance effect. What has been learned about the choice process? How large is the effect? How can the effect be parametrically modelled? What should be done to account for the effect in commercial studies?

Methods and Applications Revisited: As a synopsis of our workshop, we compare the different approaches and their contributions in specific application contexts. We investigate the emerging possibilities of applying Best-Worst Scaling Techniques. We discuss practical issues as survey and software issues.

Anmeldung über die Graduate School der Fakultät Wirtschafts- und Sozialwissenschaften der Universität Hamburg (Dr. Ulf Beckmann)