A Problem-Specific and Effective Metaheuristic for Flexibility Design

Matching uncertain demand with capacities is notoriously hard. Operations managers can use mix-flexible resources to shift excess demands to unused capacities. To find the optimal configuration of a mix-flexible production network, a flexibility design problem (FDP) is solved. Existing literature on FDPs provides qualitative structural insights, but work on solution methods is rare. We contribute the first metaheuristic which integrates these structural insights and is specifically tailored to solve FDPs. Our genetic algorithm is compared to commercial solvers on instances of up to 15 demand types, resources, and 500 demand scenarios. Experimental evidence shows that in the realistic case of flexible optimal configurations, it dominates the comparison methods regarding runtime and solution quality.Flexibility, Metaheuristic, Network Design

Estimation of distribution algorithms in logistics : Analysis, design, and application

This thesis considers the analysis, design and application of Estimation of Distribution Algorithms (EDA) in Logistics. It approaches continouos nonlinear optimization problems (standard test problems and stochastic transportation problems) as well as location problems, strategic safety stock placement problems and lotsizing problems. The thesis adds to the existing literature by proposing theoretical advances for continuous EDAs and practical applications of discrete EDAs. Thus, it should be of interest for researchers from evolutionary computation, as well as practitioners that are in need of efficient algorithms for the above mentioned problems

Developing Genetic Algorithms and Mixed Integer Linear Programs for Finding Optimal Strategies for a Student’s “Sports” Activity

An important advantage of genetic algorithms (GAs) are their ease of use, their wide applicability, and their good performance for a wide range of different problems. GAs are able to find good solutions for many problems even if the problem is complicated and its properties are not well known. In contrast, classical optimization approaches like linear programming or mixed integer linear programs (MILP) can only be applied to restricted types of problems as non-linearities of a problem that occur in many real-world applications can not appropriately modeled. This paper illustrates for an entertaining student “sports” game that GAs can easily be adapted to a problem where only limited knowledge about its properties and complexity are available and are able to solve the problem easily. Modeling the problem as a MILP and trying to solve it by using a standard MILP solver reveals that it is not solvable within reasonable time whereas GAs can solve it in a few seconds. The game studied is known to students as the so-called “beer-run”. There are different teams that have to walk a certain distance and to carry a case of beer. When reaching the goal all beer must have been consumed by the group and the winner of the game is the fastest team. The goal of optimization algorithms is to determine a strategy that minimizes the time necessary to reach the goal. This problem was chosen as it is not well studied and allows to demonstrate the advantages of using metaheuristics like GAs in comparison to standard optimization methods like MILP solvers for problems of unknown structure and complexity

Cognitive challenges in human-AI collaboration: Investigating the path towards productive delegation

We study how humans make decisions when they collaborate with an artificial intelligence (AI): each instance of a classification task could be classified by themselves or by the AI. Experimental results suggest that humans and AI who work together can outperform the superior AI when it works alone. However, this only occurred when the AI delegated work to humans, not when humans delegated work to the AI. The AI profited, even from working with low-performing subjects, but humans did not delegate well. This bad delegation performance cannot be explained with algorithm aversion. On the contrary, subjects tried to follow a provided delegation strategy diligently and appeared to appreciate the AI support. However, human results suffered due to a lack of metaknowledge. They were not able to assess their own capabilities correctly, which in turn leads to poor delegation decisions. In contrast to reluctance to use AI, lacking metaknowledge is an unconscious tr

MMB & DFT 2014 : Proceedings of the International Workshops ; Modeling, Analysis and Management of Social Networks and their Applications (SOCNET 2014) & Demand Modeling and Quantitative Analysis of Future Generation Energy Networks and Energy-Efficient Systems (FGENET 2014)

At present, a comprehensive set of measurement, modeling, analysis, simulation, and performance evaluation techniques are employed to investigate complex networks. A direct transfer of the developed engineering methodologies to related analysis and design tasks in next-generation energy networks, energy-efficient systems and social networks is enabled by a common mathematical foundation. The International Workshop on "Demand Modeling and Quantitative Analysis of Future Generation Energy Networks and Energy-Efficient Systems" (FGENET 2014) and the International Workshop on "Modeling, Analysis and Management of Social Networks and their Applications" (SOCNET 2014) were held on March 19, 2014, at University of Bamberg in Germany as satellite symposia of the 17th International GI/ITG Conference on "Measurement, Modelling and Evaluation of Computing Systems" and "Dependability and Fault-Tolerance" (MMB & DFT 2014). They dealt with current research issues in next-generation energy networks, smart grid communication architectures, energy-efficient systems, social networks and social media. The Proceedings of MMB & DFT 2014 International Workshops summarizes the contributions of 3 invited talks and 13 reviewed papers and intends to stimulate the readers’ future research in these vital areas of modern information societies.Gegenwärtig wird eine reichhaltige Klasse von Verfahren zur Messung, Modellierung, Analyse, Simulation und Leistungsbewertung komplexer Netze eingesetzt. Die unmittelbare Übertragung entwickelter Ingenieurmethoden auf verwandte Analyse- und Entwurfsaufgaben in Energienetzen der nächsten Generation, energieeffizienten Systemen und sozialen Netzwerken wird durch eine gemeinsame mathematische Basis ermöglicht. Die Internationalen Workshops "Demand Modeling and Quantitative Analysis of Future Generation Energy Net-works and Energy-Efficient Systems" (FGENET 2014) und "Modeling, Analysis and Management of Social Networks and their Applications" (SOCNET 2014) wurden am 19. März 2014 als angegliederte Symposien der 17. Internationalen GI/ITG Konferenz "Measurement, Modelling and Evaluation of Computing Systems" und "Dependability and Fault-Tolerance" (MMB & DFT 2014) an der Otto-Friedrich-Universität Bamberg in Deutschland veranstaltet. Es wurden aktuelle Forschungsfragen in Energienetzen der nächsten Generation, Smart Grid Kommunikationsarchitekturen, energieeffizienten Systemen, sozialen Netzwerken und sozialen Medien diskutiert. Der Tagungsband der Internationalen Workshops MMB & DFT 2014 fasst die Inhalte von 3 eingeladenen Vorträgen und 13 begutachteten Beiträgen zusammen und beabsichtigt, den Lesern Anregungen für ihre eigenen Forschungen auf diesen lebenswichtigen Gebieten moderner Informationsgesellschaften zu vermitteln