Card-based delivery date promising in pure flow shops with order release control

Card-based systems are simple, effective means of controlling production. Yet most systems concentrate on controlling the shop floor. They neglect other planning tasks, like estimating short, feasible due dates during customer enquiry management. A card-based version of the workload control concept for job shops – COBACABANA (COntrol of BAlance by CArd-BAsed Navigation) – was proposed in the literature to overcome this shortcoming. COBACABANA uses cards for due date setting and order release, making it a potentially important solution for small shops with limited resources. But many such firms operate as flow shops rather than job shops. Research demonstrated that COBACABANA’s release mechanism must be adapted if applied to a pure flow shop, but its approach to due date setting has not been evaluated in such an environment. We show COBACABANA has the potential to improve pure flow shop performance, but its due date setting procedure should be adapted compared to job shops. In a flow shop, due date estimation can also be further simplified by considering the load awaiting release to the first (gateway) station only while maintaining most performance benefits. The results are important for all card-based systems that aim to stabilise work-in-process, including kanban and ConWIP (Constant Work-in-Process).     

Workload control in job shops with re-entrant flows: an assessment by simulation

One of the key functions of Workload Control is order release. Jobs are not released immediately onto the shop floor – they are withheld and selectively released to create a mix of jobs that keeps work-in-process within limits and meet due dates. A recent implementation of Workload Control’s release method highlighted an important issue thus far overlooked by research: How to accommodate re-entrant flows, whereby a station is visited multiple times by the same job? We present the first study to compare the performance of Workload Control both with and without re-entrant flows. Simulation results from a job shop model highlight two important aspects: (i) re-entrant flows increase variability in the work arriving at a station, leading to a direct detrimental effect on performance; (ii) re-entrant flows affect the release decision-making process since the load contribution of all visits by a job to a station has to fit within the norm. Both aspects have implications for practice and our interpretation of previous research since: (i) parameters given for work arriving may significantly differ from those realised; (ii) increased workload contributions at release mean that prior simulations may have been unstable, leading to some jobs never being released.     

Computational homogenization of elasticity on a staggered grid

In this article, we propose to discretize the problem of linear elastic homogenization by finite differences on a staggered grid and introduce fast and robust solvers. Our method shares some properties with the FFT‐based homogenization technique of Moulinec and Suquet, which has received widespread attention recently because of its robustness and computational speed. These similarities include the use of FFT and the resulting performing solvers. The staggered grid discretization, however, offers three crucial improvements. Firstly, solutions obtained by our method are completely devoid of the spurious oscillations characterizing solutions obtained by Moulinec–Suquet's discretization. Secondly, the iteration numbers of our solvers are bounded independently of the grid size and the contrast. In particular, our solvers converge for three‐dimensional porous structures, which cannot be handled by Moulinec–Suquet's method. Thirdly, the finite difference discretization allows for algorithmic variants with lower memory consumption. More precisely, it is possible to reduce the memory consumption of the Moulinec–Suquet algorithms by 50%. We underline the effectiveness and the applicability of our methods by several numerical experiments of industrial scale. Copyright © 2015 John Wiley & Sons, Ltd.     

Investigation of charge carrier depletion in freestanding nanowires by a multi-probe scanning tunneling microscope

Profiling of the electrical properties of nanowires (NWs) and NW heterocontacts with high spatial resolution is a challenge for any application and advanced NW device development. For appropriate NW analysis, we have established a four-point prober, which is combined in vacuo with a state-of-the-art vapor-liquid-solid preparation, enabling contamination-free NW characterization with high spatial resolution. With this ultrahigh-vacuum-based multi-tip scanning tunneling microscopy (MT-STM), we obtained the resistance and doping profiles of freestanding NWs, along with surface-sensitive information. Our in-system 4-probe STM approach decreased the detection limit for low dopant concentrations to the depleted case in upright standing NWs, while increasing the spatial resolution and considering radial depletion regions, which may originate from surface changes. Accordingly, the surface potential of oxide-free GaAs NW {112} facets has been estimated to be lower than 20 mV, indicating a NW surface with very low surface state density.

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Patch layout from feature graphs

The structuring of surface meshes is a labor intensive task in reverse engineering. For example, in CAD, scanned triangle meshes must be divided into characteristic/uniform patches to enable conversion into high-level spline surfaces. Typical industrial techniques, like rolling ball blends, are very labor intensive.We provide a novel, robust and quick algorithm for the automatic generation of a patch layout based on a topology consistent feature graph. The graph separates the surface along feature lines into functional and geometric building blocks. Our algorithm then thickens the edges of the feature graph and forms new regions with low varying curvature. Further, these new regions-so-called fillets and node patches-will have highly smooth boundary curves, making the algorithm an ideal preprocessor for a subsequent spline fitting algorithm.     

Betweenness parameterized above tight lower bound

We study ordinal embedding relaxations in the realm of parameterized complexity. We prove the existence of a quadratic kernel for the Betweenness problem parameterized above its tight lower bound, which is stated as follows. For a set V of variables and set C of constraints “vi is between vj and vk”, decide whether there is a bijection from V to the set {1,…,|V|} satisfying at least |C|/3+κ of the constraints in C. Our result solves an open problem attributed to Benny Chor in Niedermeier's monograph “Invitation to Fixed-Parameter Algorithms”. The betweenness problem is of interest in molecular biology. An approach developed in this paper can be used to determine parameterized complexity of a number of other optimization problems on permutations parameterized above or below tight bounds.     

Visualizing and fuzzy filtering for discovering temporal trajectories of association rules

We propose a user-centric rule filtering method that allows to identify association rules that exhibit a certain user-specified temporal behavior with respect to rule evaluation measures. The method can considerably reduce the number of association rules that have to be assessed manually after a rule induction. This is especially necessary if the rule set contains many rules as it is the case for the task of finding rare patterns inside the data. For the proposed method, we will reuse former work on the visualization of association rules [M. Steinbrecher, R. Kruse, Visualization of possibilistic potentials, in: Foundations of Fuzzy Logic and Soft Computing, in: Lecture Notes in Comput. Sci., vol. 4529, Springer-Verlag, Berlin/Heidelberg, 2007, pp. 295–303] and use an extension of it to motivate and assess the presented filtering technique. We put the focus on rules that are induced from a data set that contains a temporal variable and build our approach on the requirement that temporally ordered sets of association rules are available, i.e., one set for every time frame. To illustrate this, we propose an ad-hoc learning method along the way. The actual rule filtering is accomplished by means of fuzzy concepts. These concepts use linguistic variables to partition rule-related domains of interest, such as the confidence change rate. The original rule sets are then matched against these user concepts and result in only those rules that match the respective concepts to a predefined extent. We provide empirical evidence by applying the proposed methods to hand-crafted as well as real-world data sets and critically discuss the current state and further prospects.