Locality, Distance Distortion, and Binary Representations of Integers

Lokalität , Distanz, Darstellun

OptiNet: Ein Optimierungswerkzeug für baumförmige Netzwerkprobleme

Optimierung , Netzwer

On the locality of Representations

Darstellungsschich

Entwicklung eines Entscheidungsunterstützungssystems für die Planung von Schnittstellentests im Rahmen der Qualitätssicherung von komponentenbasierter Unternehmenssoftware

Bei der Erstellung von komponentenbasierter Unternehmenssoftware ist es eine Aufgabe der Qualitätssicherung sicherzustellen, dass die Schnittstellen zwischen den einzelnen Komponenten keine Fehler aufweisen. Der vorliegende Beitrag beschreibt die Konzeption und Umsetzung eines Entscheidungsunterstützungssystems (EUS), welches die Qualitätssicherungsabteilung eines Anbieters von Unternehmenssoftware bei der Aufstellung eines Zeitplans für die zentrale Durchführung von Schnittstellentests unterstützt. Wichtigstes Element des EUS ist ein metaheuristikbasiertes Planungssystem, welches dem Entscheider qualitativ gute Zeitpläne vorschlägt. Das Planungssystem berücksichtigt sowohl unterschiedliche Planungsziele, als auch relevante Problemrestriktionen durch eine geeignete Wahl der Problemkodierung, einer geschickten Erzeugung des Zeitplans aus der Problemkodierung, sowie einer geeigneten Initialisierung der Startlösungen

Classification of Human Decision Behavior: Finding Modular Decision Rules with Genetic Algorithms

The understanding of human behavior in sequential decision tasks is important for economics and socio-psychological sciences. In search tasks, for example when individuals search for the best price of a product, they are confronted in sequential steps with different situations and they have to decide whether to continue or stop searching. The decision behavior of individuals in such search tasks is described by a search strategy. This paper presents a new approach of finding high-quality search strategies by using genetic algorithms (GAs). Only the structure of the search strategies and the basic building blocks (price thresholds and price patterns) that can be used for the search strategies are pre-specified. It is the purpose of the GA to construct search strategies that well describe human search behavior. The search strategies found by the GA are able to predict human behavior in search tasks better than traditional search strategies from the literature which are usually based on theoretical assumptions about human behavior in search tasks. Furthermore, the found search strategies are reasonable in the sense that they can be well interpreted, and generally that means they describe the search behavior of a larger group of individuals and allow some kind of categorization and classification. The results of this study open a new perspective for future research in developing behavioral strategies. Instead of deriving search strategies from theoretical assumptions about human behavior, researchers can directly analyze human behavior in search tasks and find appropriate and high- quality search strategies. These can be used for gaining new insights into the motivation behind human search and for developing new theoretical models about human search behavior.

Classification of Human Decision Behavior: Finding

The understanding of human behavior in sequential decision tasks is important for economics and socio-psychological sciences. In search tasks, for example when individuals search for the best price of a product, they are confronted in sequential steps with different situations and they have to decide whether to continue or stop searching. The decision behavior of individuals in such search tasks is described by a search strategy. This paper presents a new approach of finding high-quality search strategies by using genetic algorithms (GAs). Only the structure of the search strategies and the basic building blocks (price thresholds and price patterns) that can be used for the search strategies are pre- specified. It is the purpose of the GA to construct search strategies that well describe human search behavior. The search strategies found by the GA are able to predict human behavior in search tasks better than traditional search strategies from the literature which are usually based on theoretical assumptions about human behavior in search tasks. Furthermore, the found search strategies are reasonable in the sense that they can be well interpreted, and generally that means they describe the search behavior of a larger group of individuals and allow some kind of categorization and classification. The results of this study open a new perspective for future research in developing behavioral strategies. Instead of deriving search strategies from theoretical assumptions about human behavior, researchers can directly analyze human behavior in search tasks and find appropriate and high-quality search strategies. These can be used for gaining new insights into the motivation behind human search and for developing new theoretical models about human search behavior.

The Car Resequencing Problem with Pull-Off Tables

The car sequencing problem determines sequences of different car models launched down a mixed-model assembly line. To avoid work overloads of workforce, car sequencing restricts the maximum occurrence of labor-intensive options, e.g., a sunroof, by applying sequencing rules. We consider this problem in a resequencing context, where a given number of buffers (denoted as pull-off tables) is available for rearranging a stirred sequence. The problem is formalized and suited solution procedures are developed. A lower bound and a dominance rule are introduced which both reduce the running time of our graph approach. Finally, a real-world resequencing setting is investigated.mixed-model assembly line, car sequencing, resequencing

Making the Edge-Set Encoding Fly by Controlling the Bias of its Crossover Operator

The edge-set encoding is a direct tree encoding which applies search operators directly to trees represented as sets of edges. There are two variants of crossover operators for the edge-set encoding: With heuristics that consider the weights of the edges, or without heuristics. Due to a strong bias of the heuristic crossover operator towards the minimum spanning tree (MST) a population of solutions converges quickly towards the MST and EAs using this operator show low performance when used for tree optimization problems where the optimal solution is not the MST