Dynamic Reordering Research Articles

Dynamic reordering and inspection for the multi-item Inventory Record Inaccuracy problem

Inventory Record Inaccuracy (IRI) is a significant challenge in inventory management, caused by discrepancies between actual stock and inventory records due to factors such as spoilage, theft, and obsolescence. Despite extensive academic focus on robust ordering and inventory inspection planning, current literature either considers these in isolation, or focuses on stylized single-item scenarios, not addressing the full dynamics of reordering and inspection in warehouses with many SKUs. We consider such a large warehouse with several heterogeneous items that are subject to shrinkage and limited inspection opportunities. Our approach jointly optimizes reordering and inspection decisions in a dynamic decision framework, incorporating both inspection and travel times. We introduce an algorithmic pipeline for dynamic reordering and inspection that combines a neural network to decide on replenishments and a mixed-integer program to determine an optimal inspection subset. Our results show that ignoring shrinkage can increase costs by up to 95.3% and inspection already becomes viable at a relatively low level of IRI. Our proposed policy saves 8.3% in total costs compared to a static reordering and inspection benchmark, and 21.8% compared to a no-inspection policy.

Dynamic spin reordering in a hybrid layered ferrimagnet with intercalated biferrocenium radicals.

Molecule-based hybrid layered magnets provide an ideal platform for investigating the long-range spin-ordering process in low-dimensional magnetic systems. Within this context, a promising area of research is spin-sandwiched hybrid layered magnets. These materials offer the potential to explore how the spin, which is sandwiched between magnetic layers, is influenced by the internal magnetic fields generated by the magnetic layers. Herein, we report a layered ferrimagnet with intercalated biferrocenium ([bifc]+) radicals, [bifc][{Ru2(2,3,5,6-F4ArCO2)4}2(TCNQF2)] (1, TCNQF2 = 2,5-difluorotetracyano-p-quinodimethane). The [{Ru2(2,3,5,6-F4ArCO2)4}2(TCNQF2)]- moiety acts as a ferrimagnetic layer with S T = 3/2, composed of a paddlewheel [Ru2 II,II(2,3,5,6-F4ArCO2)4] (2,3,5,6-F4ArCO2 - = 2,3,5,6-tetrafluorobenzoate) with S = 1 and 2,5-difluoro-7,7,8,8-tetracyanoquinodimethanate (TCNQF2˙-) units with S = 1/2 in a 2 : 1 ratio. The isostructural paramagnetic compound [bifc][{Rh2(2,3,5,6-F4ArCO2)4}2(TCNQF2)] (2) consisting of diamagnetic [Rh2 II,II(2,3,5,6-F4ArCO2)4] components was also synthesized. An investigation of the properties of 2 revealed minimal magnetic interaction between the [bifc]+ and TCNQF2˙- components. Compound 1 displayed long-range ferrimagnetic ordering at the Curie temperature of 105 K without any frequency dependence on alternating current (AC) susceptibility, due to the combination of predominant ferrimagnetic ordering within the layer and interlayer ferromagnetic dipole interactions. However, subsequent stepwise magnetic ordering involving a strong AC frequency dependence was observed upon further cooling. These dynamic behaviors are associated with the ordering of two types of anisotropic [bifc]+ spins between the ferrimagnetic layers, indicating that [bifc]+ spin ordering is sensitive to anisotropic internal magnetic fields generated by the ferrimagnetic layers.

An introspection monitoring library to improve MPI communication time

In this paper, we describe how to improve communication time of MPI parallel applications with the use of a library that enables to monitor MPI applications and allows for introspection (the program itself can query the state of the monitoring system). Based on previous work, this library is able to see how collective communications are decomposed into point-to-point messages. It also features monitoring sessions that allow suspending and restarting the monitoring, limiting it to specific portions of the code. Experiments show that the monitoring overhead is very small and that the proposed features allow for dynamic and efficient rank reordering enabling up to 2-time reduction of communication parts of some program.

Decoding ionic conductivity and reordering in cation-disordered pyrochlores.

The ordered structure A2B2O6O' in pyrochlores engenders twin rows of inequivalent anion sublattices each centred on alternating cations. While it is known that cation antisite disorder augments the ionic conductivity by several orders of magnitude, the local cation environment around the anions and the dynamic anion reordering during the cation disordering are not well-elucidated. Using atomistic simulations on Gd2Zr2O7, we first show that the anions engage in concerted hops to the neighbouring tetrahedral sites mostly along with the 〈1 0 0〉 direction while completely avoiding the octahedral sites. While the initially vacant 8a sites start accommodating oxygen ions with increasing cation disorder, they show noticeable reluctance even at significant levels of disorder. We have also tracked both the distribution of available oxygen sites following random cation disorder, which is dependent only on cation disordering, and the probability of occupation of these sites. Interestingly, the available oxygen sites show a non-monotonic dependence on the number of B ions in the nearest neighbouring shell while the occupation probability of all the available oxygen sites increases monotonically. A tetrahedral oxygen site thus has a better probability of being occupied when it has a greater number of second neighbour B ions. This article is part of the Theo Murphy meeting issue 'Understanding fast-ion conduction in solid electrolytes'.

Iterative Variable Reordering: Taming Huge System Families

For the verification of systems using model-checking techniques, symbolic representations based on binary decision diagrams (BDDs) often help to tackle the well-known state-space explosion problem. Symbolic BDD-based representations have been also shown to be successful for the analysis of families of systems that arise, e.g., through configurable parameters or following the feature-oriented modeling approach. The state space of such system families face an additional exponential blowup in the number of parameters or features. It is well known that the order of variables in ordered BDDs is crucial for the size of the model representation. Especially for automatically generated models from real-world systems, family models might even be not constructible due to bad variable orders. In this paper we describe a technique, called iterative variable reordering, that can enable the construction of large-scale family models. We exemplify feasibility of our approach by means of an aircraft velocity control system with redundancy mechanisms modeled in the input language of the probabilistic model checker PRISM. We show that standard reordering and dynamic reordering techniques fail to construct the family model due to memory and time constraints, respectively, while the new iterative approach succeeds to generate a symbolic family model.

A Deviation-Based Dynamic Vertex Reordering Technique for 2D Mesh Quality Improvement

We propose a novel deviation-based vertex reordering method for 2D mesh quality improvement. We reorder free vertices based on how likely this is to improve the quality of adjacent elements, based on the gradient of the element quality with respect to the vertex location. Specifically, we prioritize the free vertex with large differences between the best and the worst-quality element around the free vertex. Our method performs better than existing vertex reordering methods since it is based on the theory of non-smooth optimization. The downhill simplex method is employed to solve the mesh optimization problem for improving the worst element quality. Numerical results show that the proposed vertex reordering techniques improve both the worst and average element, compared to those with existing vertex reordering techniques.

Role of Precursor Carbides for Graphene Growth on Ni(111)

Surface X-ray Diffraction was used to study the transformation of a carbon-supersaturated carbidic precursor toward a complete single layer of graphene in the temperature region below 703 K without carbon supply from the gas phase. The excess carbon beyond the 0.45 monolayers of C atoms within a single Ni2C layer is accompanied by sharpened reflections of the |4772| superstructure, along with ring-like diffraction features resulting from non-coincidence rotated Ni2C-type domains. A dynamic Ni2C reordering process, accompanied by slow carbon loss to subsurface regions, is proposed to increase the Ni2C 2D carbide long-range order via ripening toward coherent domains, and to increase the local supersaturation of near-surface dissolved carbon required for spontaneous graphene nucleation and growth. Upon transformation, the intensities of the surface carbide reflections and of specific powder-like diffraction rings vanish. The associated change of the specular X-ray reflectivity allows to quantify a single, fully surface-covering layer of graphene (2 ML C) without diffraction contributions of rotated domains. The simultaneous presence of top-fcc and bridge-top configurations of graphene explains the crystal truncation rod data of the graphene-covered surface. Structure determination of the |4772| precursor surface-carbide using density functional theory is in perfect agreement with the experimentally derived X-ray structure factors.

Efficient String Edit Similarity Join Algorithm

String similarity join is a basic and essential operation in many applications. In this paper, we investigate the problem of string similarity join with edit distance constraints. A trie-based edit similarity join framework has been proposed recently. The main advantage of existing trie-based algorithms is support for similarity join on short strings. The main problem is when joining long and distant strings. These methods generate and maintain lots of similar prefixes called active nodes which need to be further removed in a subsequent pruning phase. With large edit distance, the number of active nodes becomes quite large. In this paper, we propose a new trie-based join algorithm called PreJoin, which improves upon current trie-based join methods. It efficiently finds all similar string pairs using a novel active-node generation method, which minimizes the number of generated active nodes by applying the pruning heuristics early in the generation process. The performance of PreJoin is scaled in two different ways: First, a dynamic reordering of the trie index is used to accelerate the search for similar string pairs. Second, a partitioning method of string space is used to improve performance on large edit distance thresholds. Experiments show that our approach is highly efficient for processing short as well as long strings, and outperforms the state-of-the-art trie-based join approaches by a factor five.

A 1 A, Dual-Inductor 4-Output Buck Converter With 20 MHz/100 MHz Dual-Frequency Switching and Integrated Output Filters in 65 nm CMOS

The dual-frequency dual-inductor multiple-output (DF-DIMO) buck converter topology is presented. The topology employs a dual-phase 20 MHz current-mode-controlled input stage to reduce the inductance required per phase to only 200 nH, and a 4-output 100 MHz comparator-controlled fully integrated output stage to reduce the capacitance required per output to 10 nF. In order to enable each output to handle up to 250 mA load with less than 40 mV voltage ripple, a third-order bond-wire-based notch filter is employed at each output for voltage ripple suppression. Additionally, the proposed design employs dynamic output re-ordering to enhance cross-regulation performance, interleaved pulse-skipping to enhance light-load efficiency and dynamic performance, and high-gain error amplifier in the output feedback loop to enhance DC load regulation. Targeting multi-core DSPs, the proposed design is implemented in standard 65 nm CMOS technology with 1.8 V input, and outputs in the range of 0.6– 1.2 V with a total load of 1 A. It achieves a peak efficiency of 74%, less than 40 mV output voltage ripple, 0.5 V/70 ns dynamic voltage scaling (DVS), and settling time of less than 85 ns for 125 mA load steps; all with no observable cross-regulation transients.

Supporting Adaptive Tour with High Level Petri Nets

One of the issues for tour planning applications is to adaptively provide personalized advices for different types of tourists and tour activities. This paper proposes a high level Petri Nets based approach to providing some level of adaptation by implementing adaptive navigation in a tour node space. The new model supports dynamic reordering or removal of tour nodes along a tour path; it supports multiple travel modes and incorporates multimodality within its tour planning logic to derive adaptive tour. Examples are given to demonstrate how to realize adaptive interfaces and personalization. Future directions are also discussed at the end of this paper.

Impact of alkali acetate promoters on the dynamic ordering of PdAu catalysts during vinyl acetate synthesis

Fresh and used alkali acetate (MOAc with M+=Li+, Na+, K+, Cs+) promoted bimetallic PdAu catalysts were studied to investigate the impact of the alkali metal cations on the deactivation of the catalysts during vinyl acetate (VA) synthesis from ethene and acetic acid. Dynamic reordering of PdAu toward an active Pd1Au1 phase, a less active M2Pd2(OAc)6 and inactive Pd3(OAc)6 complexes determines the overall activity. Decreasing deactivation from LiOAc to CsOAc promoted PdAu can be associated with the lower concentration of inactive Pd3(OAc)6 leached from the active PdAu particles and with the lower loss of Cs+ compared to Li+ during reaction. CsOAc binds most efficiently to Pd3(OAc)6 and forms active Cs2Pd2(OAc)6, lowering the tendency to leach Cs+. Reduction of Pd in M2Pd2(OAc)6 to a mixed “Pd/M+–O” adlayer on the bimetallic PdAu particles stabilized active Pd0 on the PdAu surface maintaining the catalyst activity. Thus, increasing “Pd/M+–O” concentrations from LiOAc to CsOAc lower long-term deactivation of PdAu.

Effect of simultaneous helium implantation on the microstructure evolution of Inconel X-750 superalloy during dual-beam irradiation

This study focuses on investigation into the effect of helium implantation on microstructure evolution in Inconel X-750 superalloy during dual-beam (Ni+/He+) irradiation. The 1 MeV Ni+ ions with the damage rate of 10−3 dpa/s as well as 15 keV He+ ions using rate of 200 appm/dpa were simultaneously employed to irradiate specimens at 400 °C to different doses. Microstructure characterization has been conducted using high-resolution analytical transmission electron microscopy (TEM). The TEM results show that simultaneous helium injection has significant influence on irradiation-induced microstructural changes. The disordering of γ′ (Ni3 (Al, Ti)) precipitates shows noticeable delay in dose level compared to mono heavy ion irradiation, which is attributed to the effect of helium on promoting the dynamic reordering process. In contrast to previous studies on single-beam ion irradiation, in which no cavities were reported even at high doses, very small (2–5 nm) cavities were detected after irradiation to 5 dpa, which proved that helium plays crucial role in cavity formation. TEM characterization also indicates that the helium implantation affects the development of dislocation loops during irradiation. Large 1/3 〈1 1 1〉 Frank loops in the size of 10–20 nm developed during irradiation at 400 °C, whereas similar big loops detected at higher irradiation temperature (500 °C) during sole ion irradiation. This implies that the effect of helium on trapping the vacancies can help to develop the interstitial Frank loops at lower irradiation temperatures.

Efficient Control and Communication Paradigms for Coarse-Grained Spatial Architectures

There has been recent interest in exploring the acceleration of nonvectorizable workloads with spatially programmed architectures that are designed to efficiently exploit pipeline parallelism. Such an architecture faces two main problems: how to efficiently control each processing element (PE) in the system, and how to facilitate inter-PE communication without the overheads of traditional shared-memory coherent memory. In this article, we explore solving these problems using triggered instructions and latency-insensitive channels. Triggered instructions completely eliminate the program counter (PC) and allow programs to transition concisely between states without explicit branch instructions. Latency-insensitive channels allow efficient communication of inter-PE control information while simultaneously enabling flexible code placement and improving tolerance for variable events such as cache accesses. Together, these approaches provide a unified mechanism to avoid overserialized execution, essentially achieving the effect of techniques such as dynamic instruction reordering and multithreading. Our analysis shows that a spatial accelerator using triggered instructions and latency-insensitive channels can achieve 8 × greater area-normalized performance than a traditional general-purpose processor. Further analysis shows that triggered control reduces the number of static and dynamic instructions in the critical paths by 62% and 64%, respectively, over a PC-style baseline, increasing the performance of the spatial programming approach by 2.0 ×.

Atomistic Engineering of Catalyst Precursors: Dynamic Reordering of PdAu Nanoparticles during Vinyl Acetate Synthesis Enhanced by Potassium Acetate

The presence of potassium acetate (KOAc) on bimetallic PdAu catalysts increases the rate of reaction for vinyl acetate (VA) formation from ethene and acetic acid by a factor of 10 and the selectivity by 20%. The dynamic transitions of typical supported catalyst precursors with an atomic Pd/Au ratio of 2/1 were explored during synthesis in the presence and absence of KOAc. The dopant induces reordering of PdAu toward a Pd1Au1 phase, while Au-enriched Pd40Au60 bimetallic particles form primarily in the absence of KOAc. Pd–acetate species are generated via leaching of Pd from PdAu precursor particles during the reaction. These species are Pd3(OAc)6 and Pd2(OAc)4 in the absence of KOAc and K2Pd2(OAc)6 in the presence of KOAc. Palladium in K2Pd2(OAc)6 can be readily reduced by C2H4 to Pd0, while Pd3(OAc)6, which contains more stable, bridged acetate ligands remains stable. Reduced Pd either forms dispersed Pd0 or is incorporated into the metal particles. KOAc enhances rates and selectivity to VA by stabilizing...

A Forward-Checking algorithm based on a Generalised Hypertree Decomposition for solving non-binary constraint satisfaction problems

Methods exploiting hypertree decompositions are considered as the best approach for solving extensional constraint satisfaction problems (CSPs) on finite domains, with regard to theoretical time complexity when fixed widths are considered. However, this result has not been confirmed in practice because of the memory explosion problem. In this article, a new approach for efficient solving extensional non-binary CSPs is proposed. It is a combination of an enumerative search algorithm which is memory efficient and a Generalised Hypertree Decomposition (GHD) that is time efficient. This new approach is a cluster-oriented Forward-Checking algorithm. It considers the solutions of the subproblems deriving from the decomposition, as the values to be assigned rather than the values associated with the variables of the initial problem. In addition, the algorithm is guided by an order induced by the clusters deriving from the GHD. Moreover, two improved versions of this algorithm are proposed. The first version uses nogoods and the second one improves it again by a dynamic reordering of subtrees. All these algorithms have been implemented and the experimental results are promising.

BDD Ordering Heuristics for Classical Planning

Symbolic search using binary decision diagrams (BDDs) can often save large amounts of memory due to its concise representation of state sets. A decisive factor for this method's success is the chosen variable ordering. Generally speaking, it is plausible that dependent variables should be brought close together in order to reduce BDD sizes. In planning, variable dependencies are typically captured by means of causal graphs, and in preceding work these were taken as the basis for finding BDD variable orderings. Starting from the observation that the two concepts of "dependency" are actually quite different, we introduce a framework for assessing the strength of variable ordering heuristics in sub-classes of planning. It turns out that, even for extremely simple planning tasks, causal graph based variable orders may be exponentially worse than optimal. Experimental results on a wide range of variable ordering variants corroborate our theoretical findings. Furthermore, we show that dynamic reordering is much more effective at reducing BDD size, but it is not cost-effective due to a prohibitive runtime overhead. We exhibit the potential of middle-ground techniques, running dynamic reordering until simple stopping criteria hold.

Efficient Spatial Processing Element Control via Triggered Instructions

In this article, the authors present triggered instructions, a novel control paradigm for arrays of processing elements (PEs) aimed at exploiting spatial parallelism. Triggered instructions completely eliminate the program counter and allow programs to transition concisely between states without explicit branch instructions. They also allow efficient reactivity to inter-PE communication traffic. The approach provides a unified mechanism to avoid overserialized execution, essentially achieving the effect of techniques such as dynamic instruction reordering and multithreading, which each require distinct hardware mechanisms in a traditional sequential architecture.

An efficient construction and application usefulness of rectangle greedy covers

We develop efficient construction methods of a rectangle greedy cover (RGC), and evaluate its usefulness in applications. An RGC is a greedy cover of the set of given positive instances by exclusive axis-parallel hyperrectangles, namely, axis-parallel hyperrectangles that exclude all the given negative instances. An RGC is expected to be a compact classification rule with high readability because the number of its component rectangles is expected to be small and it can be seen as a disjunctive normal form, which is one of the most readable representations for us.We propose two approaches of RGC construction: enumeration approach and direct approach. In enumeration approach, the maximal exclusive positive subsets (MEPSs) are enumerated first and then an ordinary greedy set covering is done using the enumerated MEPSs. We make clear the relation between enumeration of the maximal frequent itemsets and enumeration of the MEPSs, and convert an efficient enumeration algorithm LCMmax [1] of maximal frequent itemsets to an enumeration algorithm LCMmax.Rnaive of MEPSs. We also develop a more efficient version of LCMmax.Rnaive, or LCMmax.R, by incorporating effective dynamic reordering of instances using excluded frequency and bit-parallel exclusiveness check. In direct approach, each component MEPS of an RGC is searched not from enumerated MEPSs but directly from the dataset that consists of the remaining uncovered positive instances and the whole negative instances. We developed an algorithm called MRF that efficiently finds an maximum-sized MEPS for given positive and negative instances. MRF is made from LCMmax.R by modifying it so as to find a maximum-sized MEPS only. An RGC is constructed by MRF repetition, that is, by repeatedly executing MRF using the remaining uncovered positive instances.According to our experimental evaluation using UCI-repository datasets, LCMmax.R was about 5–11 times faster than LCMmax.Rnaive, which indicates effectiveness of the introduced two improvements. MRF repetition, however, was significantly faster than LCMmax.R, and it was fast enough for practical usage. The experimental results using UCI-repository datasets also showed that accuracy of a nearest rectangle classifier using an RGC is close to that using the hyperrectangles output by the randomized subclass method (RSM) [2] though the number of component rectangles of an RGC is significantly smaller than the number of the hyperrectangles output by RSM. The performance of RGC was also shown to be comparable to that of the six popular classifiers including logistic regression and support vector machine. The disjunctive normal form representation of the classification rules obtained by RGC was demonstrated to be simpler and more readable for us than that obtained by RSM and C4.5.

Dynamic Channel Reordering to Reduce Latency During Surfing Periods in IPTV Networks

In IPTV networks, channel change latency represents a major concern in achieving broadcast-level service quality. Bandwidth limitations at the user side prevents the clients from receiving all the channels at once. As a result, most of the requests have to go through the network leading to significant delays during the channel change process. In this paper, we address this latency problem in IPTV networks by proposing a novel channel reordering technique that exploits the differing key-frame delivery times for the adjacent sessions to dynamically arrange the switching order during the surfing periods. The simulation results show that, compared to static channel ordering techniques, the proposed framework can achieve more than 50% improvement in channel change latency without introducing any significant overhead in the network. The proposed dynamic reordering technique is also shown to be robust against variations observed in channel listings and/or session time-shifts.

Dynamic Minimization of Sentential Decision Diagrams

The Sentential Decision Diagram (SDD) is a recently proposed representation of Boolean functions, containing Ordered Binary Decision Diagrams (OBDDs) as a distinguished subclass. While OBDDs are characterized by total variable orders, SDDs are characterized more generally by vtrees. As both OBDDs and SDDs have canonical representations, searching for OBDDs and SDDs of minimal size simplifies to searching for variable orders and vtrees, respectively. For OBDDs, there are effective heuristics for dynamic reordering, based on locally swapping variables. In this paper, we propose an analogous approach for SDDs which navigates the space of vtrees via two operations: one based on tree rotations and a second based on swapping children in a vtree. We propose a particular heuristic for dynamically searching the space of vtrees, showing that it can find SDDs that are an order-of-magnitude more succinct than OBDDs found by dynamic reordering.