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Öğe Minimizing mean flow time in a flowshop scheduling with learning effect(2004) Eren T.; Güner E.The phenomenon of the learning effect has been extensively studied in many different areas of operational research. However, there have been very few studies in the general context of production scheduling. These studies were about on one and parallel machines but there has never been investigated in multi-machine flowshop scheduling settings. We focus in this paper on flowtime minimization with learning effect on a two-machine flowshop case. To solve this problem, we formulate an integer programming model with n 2 + 3n variables and 4n constraints where n is the number of jobs. We show that the integer programming model is effective in solving problems with up to 54 jobs.Öğe Setup times with a learning effect in flowshop scheduling problem(2007) Eren T.; Güner E.Relevant to recent scheduling studies, setup times of jobs have generally been either neglected or considered in processing times. But, in some production systems the setup times may be so large that it can not be neglected but should be considered separately from processing times. In production systems, since jobs are generally processed on automated machines, job processing times are independent of the sequence of jobs. When the setup times are taken into account, there will be a gradual decline in their magnitudes due to the repetition of setup procedures by human operators. This phenomenon is known as the learning effect in scheduling analysis. In this study, a two-machine flow-shop scheduling problem with learning effect setup times is considered. A mathematical programming model is developed for the problem and according to different setup time ranges and learning effects, computational results are compared. Additionally, heuristic approaches are presented and experimental results are given for large size problems.Öğe Using angle of arrival (bearing) information for localization in robot networks(2007) Eren T.In this paper, we consider using angle of arrival information (bearing) for localization in robot networks. The essential property we require in this paper is that a node can infer heading information from its neighbors. We address the uniqueness of network localization solutions by the theory of globally rigid graphs. We show that while the parallel rigidity problem for formations with bearings is isomorphic to the distance case, the global rigidity of the formation is simpler (in fact identical to the simpler rigidity case) for a network with bearings, compared to formations with distances. We provide the conditions of localization for networks in which the neighbor relationship is not necessarily symmetric. © TÜBİTAK.
