Dornberger, Rolf
E-Mail-Adresse
Geburtsdatum
Suchergebnisse
Quantum computing in supply chain management state of the art and research directions
Quantum computing is the most promising computational advance of the coming decade for solving the most challenging problems in supply chain management and logistics. This paper reviews the state-of-the-art of quantum computing and provides directions for future research. First, general concepts relevant to quantum computers and quantum computing are introduced. Second, the dominating quantum technologies are presented. Third, the quantum industry is analyzed, and recent applications in different fields of supply chain management and logistics are illustrated. Fourth, directions for future research are given. We hope this review to educate and inspire the use of quantum computing in the fields of optimization, artificial intelligence, and machine learning for supply chain and logistics.
Wearables for integrative performance and tactic analyses: opportunities, challenges, and future directions
Micro-electromechanical systems (MEMS) have reduced drastically in size, cost, and power consumption, while improving accuracy. The combination of different sensor technologies is considered a promising step in the monitoring of athletes. Those “wearables” enable the capturing of relevant physiological and tactical information in individual and team sports and thus replacing subjective, time-consuming and qualitative methods with objective, quantitative ones. Prior studies mainly comprised sports categories such as: targeting sports, batting and fielding games as well as net and wall games, focusing on the detection of individual, non-locomotive movements. The increasing capabilities of wearables allow for more complex and integrative analysis expanding research into the last category: invasion sports. Such holistic approaches allow the derivation of metrics, estimation of physical conditions and the analysis of team strategic behavior, accompanied by integrative knowledge gains in technical, tactical, physical, and mental aspects of a sport. However, prior and current researchers find the precise measurement of the actual movement within highly dynamic and non-linear movement difficult. Thus, the present article showcases an overview of the environments in which the wearables are employed. It elaborates their use in individual as well as team-related performance analyses with a special focus on reliability and validity, challenges, and future directions.
Optimization of artificial landscapes with a hybridized firefly algorithm
This paper shows how the metaheuristic Firefly Algorithm (FA) can be enhanced by hybridization with a genetic algorithm to achieve better results for optimization problems. The authors examine which configuration of the hybridized FA performs best during a number of computational tests. The performance of the hybrid FA is compared with that of the regular FA in solving test functions for single-objective optimization problems in two and n-dimensional spaces. The key findings are that more complex optimization problems benefit from the hybrid FA because it outperforms the basic FA. In addition, some useful parameters settings for the suggested algorithm are determined.
Effects of noisy multiobjective test functions applied to evolutionary optimization algorithms
In this paper we study the effects of noise in multiobjective optimization problems. We consider a test function, which may be affected by noise with different strength and frequency of occurrence. To simplify the analysis, the noise is applied to only one of the objective functions, i.e. one of the objective functions is affected by additional random influences. Three different evolutionary algorithms for multiobjective problems are analyzed in this way: the Covariance Matrix Adaption Evolution Strategy (CMA-ES), the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), and the Particle Swarm Optimization (PSO). The results are presented and analyzed with respect to the resulting Pareto fronts and with respect to the distribution of variable values during the algorithm run. It can be observed that all three algorithms are basically able to derive suitable results. However, only PSO leads to a sparse Pareto front in case of noisy and non-noisy situations while CMA and NSGA-II perform similarly well. In some situations for NSGA-II and more clearly for CMA-ES specific patterns for the variable values (denoted as striae in this paper) can be observed which appear to be partly caused by the noise.
Multiobjective evolutionary algorithm for the optimization of noisy combustion processes
Robotic path planning by Q learning and a performance comparison with classical path finding algorithms
Q Learning is a form of reinforcement learning for path finding problems that does not require a model of the environment. It allows the agent to explore the given environment and the learning is achieved by maximizing the rewards for the set of actions it takes. In the recent times, Q Learning approaches have proven to be successful in various applications ranging from navigation systems to video games. This paper proposes a Q learning based method that supports path planning for robots. The paper also discusses the choice of parameter values and suggests optimized parameters when using such a method. The performance of the most popular path finding algorithms such as A* and Dijkstra algorithm have been compared to the Q learning approach and were able to outperform Q learning with respect to computation time and resulting path length.
Comparison of the behavior of swarm robots with their computer simulations applying target-searching algorithms
This paper investigates the functionality and quality of the implementation of a search and target surrounding swarm robotic algorithm using physical swarm robots named Kilobots. The implementation was developed and tested in the simulator V-REP, then transferred onto the actually running Kilobots: Ten Kilobots were used for the experiment, where one Kilobot acts as the target and nine Kilobots act as the searchers. The algorithm allows the searchers to swarm out to find the target while avoiding collisions with other searchers, to orbit around other searchers, which are closer to the target, and finally to surround the target once it is found. The results of the implementation using the physical Kilobots are compared with the results of two adjusted computer simulations. Differences between the simulations and the real robot implementation are investigated: Discrepancies regarding the locomotion and the communication capabilities are identified and discussed.