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The development of a communication antenna employing a sequential phased feeding network and a 4x4 array antenna operating in the X-band is detailed in this article. The proposed antenna implemented a coaxial probe feeding method in its feeding network, which aimed to enhance the gain and axial ratio bandwidth. Each patch is circular-shaped, with a triangular negative perturbation and an elliptical ring slot at the center of the patch radiator. The bandwidth of the reflection coefficient and axial ratio goals must be up to 0.4 GHz. The paper also discusses the comparison between the microstrip feeding networks and the coaxial probe feeding networks. In comparison, coaxial probe feeding can produce better gain, which was up to 12 dBic at the center frequency (8.2 GHz), while microstrip feeding produced nothing but 9.7 dBic at the center frequency (8.2 GHz). The NPC-H220A, with a thickness of 1.6 mm, a dielectric constant of 2.17, and a loss tangent of 0.005, printed the antenna design with a total dimension of 123.7x123.7 mm. The antenna design achieves the required performance and achieves a radiation efficiency of >95%. The angular width is about 22.8°, and the side-lobe level is -21.4 dB. The proposed antenna is suitable for wideband communication applications. [ABSTRACT FROM AUTHOR]

Research Summary: Strategy formation is central to why some firms seize novel opportunities while others fail. We explore a core dilemma of strategy formation in entrepreneurial settings—whether to learn a novel strategy one domain at a time (modular) versus assemble a complex strategy of coherent activities across all domains at once (integrative). By studying six ventures, we develop a theoretical framework for how entrepreneurs effectively form novel, complex strategy: Decision weaving. They (a) employ sequential focus (not parallel), (b) pause at plateaus (not optima), and (c) deploy stepping stones (not leaps) in background domains. These behaviors enable both fast, effective learning and evolving yet holistic understanding of an emerging strategy. Overall, we contribute to the microfoundations of strategy by proposing a cognitively sophisticated, yet realistic strategist. Managerial Summary Strategy formation is central to why firms seize novel opportunities while others fail. By comparing three venture‐pairs, we develop a fresh framework for strategy formation in nascent markets where strategy is both novel and complex: Decision weaving. Effective strategists: (a) use sequential focus (not parallel) to learn about successive focal strategic domains, (b) pause at learning plateaus to consolidate that knowledge about a focal domain, and (c) use stepping stones to make progress in background domains without losing focus. These behaviors enable both fast, effective learning, and evolving yet holistic understanding of an emerging strategy. More importantly, these behaviors set the stage for rapid and profitable scaling (i.e., growth). [ABSTRACT FROM AUTHOR]

The spatial organization of chromatin has become recognized as a major factor in the regulation of gene expression. While chromosome conformation capture techniques, such as high-throughput chromosome conformation capture (Hi-C), have advanced our understanding of the role of 3D organization of chromatin on chromosomal function, Hi-C data are typically ensemble in nature and relies on intense computational methods to derive genomic structural information. The field is beginning to shift in recognizing the potential value of imaging methods to study spatial organization of chromatin. Imaging allows acquisition and analysis at the individual cell level while providing direct visualization and analysis of 3D chromatin organization. This feature will highlight how single-molecule localization microscopy (SMLM) using the Bruker Vutara SMLM system in conjunction with sequential labeling via microfluidics is revolutionizing our understanding of nuclear spatial organization and its role in regulation of gene function. [ABSTRACT FROM AUTHOR]

This paper investigates a new scheduling problem of multiple orders per job (MOJ) in a three-machine flowshop consisting of an item-processing machine, a lot-processing machine and a batch-processing machine, for a semiconductor manufacturing operation that must form a layer on the wafers. The three-machine flowshop MOJ scheduling problem deals with sequencing customer orders, assigning orders to jobs, and then combining the formed jobs into batches. Genetic algorithms are presented for this scheduling problem to minimise the total weighted tardiness (TWT), in the presence of non-zero order ready times, different order due dates, different order weights and unequal order sizes. Extensive experiments were performed to compare the proposed genetic algorithm (GA)-based approach with the benchmark heuristics presented in previous studies. The experiments led to the conclusions that the GA-based approach is significantly superior over other heuristics in terms of TWT and can find near-optimal solutions in an acceptable amount of computation time. [ABSTRACT FROM AUTHOR]

This paper presents a simulation-based experimental study of scheduling rules for scheduling a dynamic flexible flow line problem considering sequence-dependent setup times. A discrete-event simulation model is presented as well as eight adapted heuristic algorithms, including seven dispatching rules and one constructive heuristic, from the literature. In addition, six new proposed heuristics are implemented in the simulation model. Simulation experiments are conducted under various conditions such as setup time ratio and shop utilisation percentage. One of the proposed rules performs better for the mean flow time measure and another one performs better for the mean tardiness measure. Finally, multiple linear regression based meta-models are developed for the best performing scheduling rules. [ABSTRACT FROM AUTHOR]

In paint shops in the automotive industry, a change of colours between two consecutive cars causes sequence-dependent set-up costs. Often, selectivity banks are used as storage and retrieval systems to create colour-oriented batches of cars before the paint application in order to reduce set-up costs. The efficient control of those selectivity banks is considered a major problem of operating paint shop systems. A branch&bound approach for the colour batching process is presented. Some computational results obtained with the approach are described. An additional focus is to outline the characteristics of parts of the colour-batching problem in selectivity banks as a sequential ordering problem. [ABSTRACT FROM AUTHOR]

This paper addresses job shop scheduling with sequence dependent family set-ups. Based on a simple, single-machine dynamic scheduling problem, state dependent scheduling rules for the single machine problem are developed and tested using Markov Decision Processes. Then, a generalized scheduling policy for the job shop problem is established based on a characterization of the optimal policy. The policy is combined with a 'forecasting' mechanism to utilize global shop floor information for local dispatching decisions. Computational results show that performance is significantly better than that of existing alternative policies. [ABSTRACT FROM AUTHOR]

Relatively few studies have examined the importance of an entry's sequential position to the choice of foreign entry mode. We use a dynamic model to analyze sequential entries into the United States from 1975 to 1992. Our findings show that several independent variables which explain a firm's initial mode of entry do not explain the modes of subsequent entries. These findings underscore the importance of experience in foreign investment, as companies learn from early entries and adapt the modes of subsequent ones. [ABSTRACT FROM AUTHOR]

The GOAL SYSTEMTM implements a drum-buffer-rope (DBR) approach to production scheduling. Earlier versions of the software were only capable of building a schedule for one constrained resource at a time. If multiple resources constrained the overall system's capability, the schedules for these resources had to be created sequentially. However, the current version contains an ability to simultaneously create multiple constraint schedules. A set of solutions was generated for benchmark problems developed in previous research. The new results were compared to solutions produced by the sequential algorithm, as well as solutions which optimally minimize maximum tardiness. The simultaneous algorithm was found to slightly reduce maximum tardiness, but performed worse with respect to average flow-time, percentage of tardy jobs and total days late. [ABSTRACT FROM AUTHOR]

A computer simulation of an assembly line enforcing pacing on manned sequential stations indicates that unpaced station operations are superior to paced. Using the variability and work-time distribution of operator performance on repetitive tasks, a number of studies were carried out which show that an assembly line cannot perform at maximum efficiency (in terms of operator idle time and units completed) unless queues are provided before each work station. Studies of various station speed configurations indicate that the generally preferred configuration is Fast-Medium-Slow (short-medium-long cycle time) and that there is little difference among various configurations when the inter-arrival time is larger than any of the operator mean cycle times. [ABSTRACT FROM AUTHOR]

The middle route of the South-to-North Water Diversion project alleviates drought in northern China, especially reducing water shortage pressure in Beijing. However, after submersion, the potential release risk of metals in newly submerged soils into the water in the receiving reservoir remains unclear. Here, we assess the labile Zn in the riparian soils of Miyun Reservoir (MYR) using the diffusive gradients in thin films (DGT), DGT-induced fluxes in soils (DIFS) model, and Community Bureau of Reference (BCR) sequential extraction. The results showed that the average Zn concentrations at three sampling sites (S2, S3, and S5) exceeded soil background value (74.8 mg/kg), indicative of Zn accumulation in the MYR. The concentrations of DGT-labile Zn varied within 39.7–62.4 μg/L (average: 56.7 μg/L), with the greatest value observed at 145 m at sampling site S3, attributed to anthropogenic activities in recreational areas. The DGT-labile Zn showed no correlation with classes of land, elevations, or soil properties. Sequential extraction results demonstrated that Zn predominantly existed in the residual fraction, but still showed a strong capability for resupply from the solid phase (R >1). The DIFS model simulation results showed that Zn underwent irreversible diffusion of intra-particle metals from the solid phase to the soil solution. Therefore, the potential release risk of labile Zn in riparian soils in MYR cannot be ignored, especially for areas experiencing human disturbance. [ABSTRACT FROM AUTHOR]

In the US, the demand for outpatient services is expected to increase, while the supply of physicians to provide the care is projected to decrease. Besides, inefficiencies in the appointment system (AS) and patient no-shows (patients who do not arrive for scheduled appointments) reduce provider productivity, timely access to care, and cost the U.S. healthcare system more than $150 billion a year. To handle increasing demand and compensate for patient no-shows, outpatient clinics tend to overbook appointments. The current scheduling practice at most clinics and majority of the scheduling rules proposed in the literature assume all patients are equally likely to miss an appointment. Further, most scheduling rules in the literature do not make use of the available data, such as electronic health records, when scheduling patients. This paper proposes a prescriptive analytics framework to improve the performance of an AS with respect to patient satisfaction (measured using average patient waiting time and number of patients unable to get an appointment for the day under consideration) and resource utilization (measured using average resource idle time, overflow time and overtime). In the proposed framework, patient-related data from various sources are used to develop predictive models that identify the risk of a patient no-show. Different scheduling rules, that leverage the patient-specific no-show risk is then proposed. A case study, with real data from a Family Medicine Clinic in Pennsylvania, is used to show the feasibility of the proposed framework. The effectiveness of the proposed scheduling rules is evaluated by benchmarking it with three rules adapted from the literature. The results indicate that the proposed scheduling rules consistently outperform the benchmark rules for all the clinic settings tested. Further, the proposed framework is generic and can be adopted by any outpatient clinic characterized by occurrences of no-shows and appointment-based customer arrivals. [ABSTRACT FROM AUTHOR]

Despite major strides in the treatment of cancer, the development of drug resistance remains a major hurdle. One strategy which has been proposed to address this is the sequential application of drug therapies where resistance to one drug induces sensitivity to another drug, a concept called collateral sensitivity. The optimal timing of drug switching in these situations, however, remains unknown. To study this, we developed a dynamical model of sequential therapy on heterogeneous tumors comprised of resistant and sensitive cells. A pair of drugs (DrugA, DrugB) are utilized and are periodically switched during therapy. Assuming resistant cells to one drug are collaterally sensitive to the opposing drug, we classified cancer cells into two groups, AR and BR, each of which is a subpopulation of cells resistant to the indicated drug and concurrently sensitive to the other, and we subsequently explored the resulting population dynamics. Specifically, based on a system of ordinary differential equations for AR and BR, we determined that the optimal treatment strategy consists of two stages: an initial stage in which a chosen effective drug is utilized until a specific time point, T, and a second stage in which drugs are switched repeatedly, during which each drug is used for a relative duration (i.e., fΔt-long for DrugA and (1-f)Δt-long for DrugB with 0≤f≤1 and Δt≥0). We prove that the optimal duration of the initial stage, in which the first drug is administered, T, is shorter than the period in which it remains effective in decreasing the total population, contrary to current clinical intuition. We further analyzed the relationship between population makeup, A/B=AR/BR, and the effect of each drug. We determine a critical ratio, which we term (A/B)∗, at which the two drugs are equally effective. As the first stage of the optimal strategy is applied, A/B changes monotonically to (A/B)∗ and then, during the second stage, remains at (A/B)∗ thereafter. Beyond our analytic results, we explored an individual-based stochastic model and presented the distribution of extinction times for the classes of solutions found. Taken together, our results suggest opportunities to improve therapy scheduling in clinical oncology. [ABSTRACT FROM AUTHOR]

The sparse triangular solve kernels, SpTRSV and SpTRSM, are important building blocks for a number of numerical linear algebra routines. Parallelizing SpTRSV and SpTRSM on today's manycore platforms, such as GPUs, is not an easy task since computing a component of the solution may depend on previously computed components, enforcing a degree of sequential processing. As a consequence, most existing work introduces a preprocessing stage to partition the components into a group of level-sets or colour-sets so that components within a set are independent and can be processed simultaneously during the subsequent solution stage. However, this class of methods requires a long preprocessing time as well as significant runtime synchronization overheads between the sets. To address this, we propose in this paper novel approaches for SpTRSV and SpTRSM in which the ordering between components is naturally enforced within the solution stage. In this way, the cost for preprocessing can be greatly reduced, and the synchronizations between sets are completely eliminated. To further exploit the data-parallelism, we also develop an adaptive scheme for efficiently processing multiple right-hand sides in SpTRSM. A comparison with a state-of-the-art library supplied by the GPU vendor, using 20 sparse matrices on the latest GPU device, shows that the proposed approach obtains an average speedup of over two for SpTRSV and up to an order of magnitude speedup for SpTRSM. In addition, our method is up to two orders of magnitude faster for the preprocessing stage than existing SpTRSV and SpTRSM methods. [ABSTRACT FROM AUTHOR]

Little is known about how people structure sets of visualizations to support sequential viewing. We contribute findings from several studies examining visualization sequencing and reception. In our first study, people made decisions between various possible structures as they ordered a set of related visualizations (consisting of either bar charts or thematic maps) into what they considered the clearest sequence for showing the data. We find that most people structure visualization sequences hierarchically: they create high level groupings based on shared data properties like time period, measure, level of aggregation, and spatial region, then order the views within these groupings. We also observe a tendency for certain types of similarities between views, like a common spatial region or aggregation level, to be seen as more appropriate categories for organizing views in a sequence than others, like a common time period or measure. In a second study, we find that viewers' perceptions of the quality and intention of different sequences are largely consistent with the perceptions of the users who created them. The understanding of sequence preferences and perceptions that emerges from our studies has implications for the development of visualization authoring tools and sequence recommendations for guided analysis. [ABSTRACT FROM AUTHOR]

In cloud computing, data or resources are provided to the user by paying for the share of resources used by them. For efficient allocation of resources to the appropriate user, an efficient task scheduling algorithm is required, which can achieve minimum response time, high throughput and proper resource utilization, etc. Heuristic based algorithm is one such way to achieve the optimal or near optimal solution of task scheduling in the cloud environment. Heuristic methods are the subset of meta-heuristic approach. In this paper, various type of task scheduling algorithms in cloud computing have been surveyed. [ABSTRACT FROM AUTHOR]

This paper presents an overview of various nature-inspired optimization algorithms for scheduling problems. Nature is a vast source of inspiration to solve complex problems (NP-hard) in computer science as it shows extremely diverse, dynamic, robust, complex and fascinating techniques. It helps to find the optimal solution in order to solve the problem keeping perfect balance among its components. Nature inspired algorithms are meta-heuristics which are motivated by the nature in order to solve various optimization problems. For the past decades, great research efforts has been concentrated on these techniques. The surprising results increase the scope and practicality of these meta-heuristic techniques exploring new areas of application and more opportunities in computing. [ABSTRACT FROM AUTHOR]

Hadoop is a structure for BigData handling in distributed applications. Hadoop bunch is worked for running information intensive distributed applications. Hadoop distributed file system is the essential stockpiling territory for BigData. MapReduce is a model to total undertakings of a job. Task assignment is conceivable by schedulers. Schedulers ensure the reasonable assignment of assets among clients. At the point when a client presents a job, it will move to a job queue. From the job queue, job will be divided into tasks and distributed to various nodes. By the correct assignment of tasks, job finish time will decrease. This can guarantee better execution of the job. This paper gives the comparison of different Hadoop Job Schedulers. [ABSTRACT FROM AUTHOR]

The pickup and delivery problem with time windows is an NP-hard discrete optimization problem with two objectives-to minimize the fleet serving transportation requests, and to minimize the distance traveled during this service. Although there exist exact algorithms for tackling this problem, they are still difficult to apply in massively large practical scheduling scenarios due to their time complexities. Hence, the approximate methods became the main stream of research in this field. In this paper, we propose an adaptive guided ejection search algorithm for solving the pickup and delivery with time windows. The pivotal part of this technique is the pre-processing step, in which the instance characteristics concerning its underlying structure are extracted in the clustering and histogram-based analyses. Then, the k-nearest neighbor algorithm is applied to classify the instance to an appropriate class. Finally, the most suitable variant of our enhanced guided ejection search algorithm is adaptively chosen for solving this instance based on the classification outcome. An extensive experimental study performed on the full Li and Lim's benchmark (encompassing 354 problem instances belonging to 6 classes) revealed that our pre-processing allows for achieving very high classification accuracy, thus for selecting the best variant of the enhanced guided ejection search. [ABSTRACT FROM AUTHOR]

The objective of this paper is to give a tool for the practical users, looking for the efficient way for solving pathfinding problem, concerning planning the best paths for the simultaneously moving agents in the crowded environment with obstacles. The proposed approach is based on the two-stage approach. In the first stage, a navigation mesh for passable regions in rectangular 2D environment is created using Quad-trees algorithm. In the second stage, a path is found for each agent present in environment using Dijkstra or A* algorithm. To find efficient paths in crowded environment, density information for each passable region is stored. Density information is further mapped on graph edges along with the distance values. The key point is that the moving agents reevaluate their paths accordingly to the re-planning strategy. Three strategies are considered: (i) periodical re-planning, (ii) periodical with initial re-planning, and (iii) the proposed way called event-driven re-planning. The created and implemented experimentation system can be adopted by the practical user for testing the twostage combinations of algorithms. The results of investigations, based on simulation experiments made with this system, presented in the paper, showed that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

A good ship traffic scheduling mode can enhance traffic efficiency to a large degree, especially in restricted waterways such as approach channel of ports and straits. A ship scheduling model for restricted two-way waterways is proposed in this article. The assumption is made in the model that two-way transportation is not allowed for cases in which there is at least one large ship. Based on this assumption, a mathematical model is proposed to minimize the weighted average of the mean and the maximum waiting time given several safety restrictions. A sequential scheduling algorithm is proposed to solve such problem, in which the ships are divided into rounds. The ships in each round are scheduled simultaneously to enhance traffic efficiency by the trade-off between the priorities of small and large ships. Experiments are carried out by comparing sequential scheduling method with first-come-first-serve model. The results indicate that the proposed algorithm can reduce ships’ waiting time, whereas a lot of ships need to wait for long time with first come first serve. The distinctions are more evident with a higher proportion of large ships, and the proposed algorithm can keep ships’ waiting time to an acceptable level. [ABSTRACT FROM AUTHOR]

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There are billions of lines of sequential code inside nowadays' software which do not benefit from the parallelism available in modern multicore architectures. Automatically parallelizing sequential code, to promote an efficient use of the available parallelism, has been a research goal for some time now. This work proposes a new approach for achieving such goal. We created a new parallelizing compiler that analyses the read and write instructions, and control-flow modifications in programs to identify a set of dependencies between the instructions in the program. Afterwards, the compiler, based on the generated dependencies graph, rewrites and organizes the program in a task-oriented structure. Parallel tasks are composed by instructions that cannot be executed in parallel. A work-stealing-based parallel runtime is responsible for scheduling and managing the granularity of the generated tasks. Furthermore, a compile-time granularity control mechanism also avoids creating unnecessary data-structures. This work focuses on the Java language, but the techniques are general enough to be applied to other programming languages. We have evaluated our approach on 8 benchmark programs against OoOJava, achieving higher speedups. In some cases, values were close to those of a manual parallelization. The resulting parallel code also has the advantage of being readable and easily configured to improve further its performance manually. [ABSTRACT FROM AUTHOR]

We analyze a sequential decision making process, in which at each step the decision is made in two stages. In the first stage a partially optimal action is chosen, which allows the decision maker to learn how to improve it under the new environment. We show how inertia (cost of changing) may lead the process to converge to a routine where no further changes are made. We illustrate our scheme with some economic models. [ABSTRACT FROM AUTHOR]

Although Latin square is a well-known algorithm to construct low-density parity-check (LDPC) codes for satisfying long code length, high code-rate, good correcting capability, and low error floor, it has a drawback of large submatrix that the hardware implementation will be suffered from large barrel shifter and worse routing congestion in fitting NAND flash applications. In this paper, a top-down design methodology, which not only goes through code construction and optimization, but also hardware implementation to meet all the critical requirements, is presented. A two-step array dispersion algorithm is proposed to construct long LDPC codes with a small submatrix size. Then, the constructed LDPC code is optimized by masking matrix to obtain better bit-error rate (BER) performance and lower error-floor. In addition, our LDPC codes have a diagonal-like structure in the parity-check matrix leading to a proposed hybrid storage architecture, which has the advantages of better area efficiency and large enough data bandwidth for high decoding throughput. To be adopted for NAND flash applications, an (18900, 17010) LDPC code with a code-rate of 0.9 and submatrix size of 63 is constructed and the field-programmable gate array simulations show that the error floor is successfully suppressed down to BER of 10^-12 . An LDPC decoder using normalized min-sum variable-node-centric sequential scheduling decoding algorithm is implemented in UMC 90-nm CMOS process. The postlayout result shows that the proposed LDPC decoder can achieve a throughput of 1.58 Gb/s at six iterations with a gate count of 520k under a clock frequency of 166.6 MHz. It meets the throughput requirement of both NAND flash memories with Toggle double data rate 1.0 and open NAND flash interface 2.3 NAND interfaces. [ABSTRACT FROM AUTHOR]

A novel adaptive surrogate modeling-based algorithm is proposed to solve the integrated scheduling and dynamic optimization problem for sequential batch processes. The integrated optimization problem is formulated as a large scale mixed-integer nonlinear programming (MINLP) problem. To overcome the computational challenge of solving the integrated MINLP problem, an efficient solution algorithm based on the bilevel structure of the integrated problem is proposed. Because processing times and costs of each batch are the only linking variables between the scheduling and dynamic optimization problems, surrogate models based on piece-wise linear functions are built for the dynamic optimization problems of each batch. These surrogate models are then updated adaptively, either by adding a new sampling point based on the solution of the previous iteration, or by doubling the upper bound of total processing time for the current surrogate model. The performance of the proposed method is demonstrated through the optimization of a multiproduct sequential batch process with seven units and up to five tasks. The results show that the proposed algorithm leads to a 31% higher profit than the sequential method. The proposed method also outperforms the full space simultaneous method by reducing the computational time by more than four orders of magnitude and returning a 9.59% higher profit. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4191-4209, 2015 [ABSTRACT FROM AUTHOR]

We consider the problem of scheduling a given set of n jobs with equal processing times on m parallel machines so as to minimize the makespan. Each job has a given release date and is compatible to only a subset of the machines. The machines are ordered and indexed in such a way that a higher-indexed machine can process all the jobs that a lower-indexed machine can process. We present a solution procedure to solve this problem in O(n2+mnlogn) time. We also extend our results to the tree-hierarchical processing sets case and the uniform machine case. [ABSTRACT FROM AUTHOR]

Microarchitecture research and development rely heavily on simulators. The ideal simulator should be simple and easy to develop, it should be precise, accurate and very fast. But the ideal simulator does not exist, and microarchitects use different sorts of simulators at different stages of the development of a processor, depending on which is most important, accuracy or simulation speed. Approximate microarchitecture models, which trade accuracy for simulation speed, are very useful for research and design space exploration, provided the loss of accuracy remains acceptable. Behavioral superscalar core modeling is a possible way to trade accuracy for simulation speed in situations where the focus of the study is not the core itself. In this approach, a superscalar core is viewed as a black box emitting requests to the uncore at certain times. A behavioral core model can be connected to a detailed uncore model. Behavioral core models are built from detailed simulations. Once the time to build the model is amortized, important simulation speedups can be obtained. We describe and study a new method for defining behavioral models for modern superscalar cores. The proposed Behavioral Application-Dependent Superscalar Core model, BADCO, predicts the execution time of a thread running on a superscalar core with an error less than 10 % in most cases. We show that BADCO is qualitatively accurate, being able to predict how performance changes when we change the uncore. The simulation speedups we obtained are typically between one and two orders of magnitude. [ABSTRACT FROM AUTHOR]

The COSMIC-2 mission is a follow-on mission of the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) with an upgraded payload for improved radio occultation (RO) applications. The objective of this paper is to develop a near-real-time (NRT) orbit determination system, called NRT National Chiao Tung University (NCTU) system, to support COSMIC-2 in atmospheric applications and verify the orbit product of COSMIC. The system is capable of automatic determinations of the NRT GPS clocks and LEO orbit and clock. To assess the NRT (NCTU) system, we use eight days of COSMIC data (March 24-31, 2011), which contain a total of 331 GPS observation sessions and 12 393 RO observable files. The parallel scheduling for independent GPS and LEO estimations and automatic time matching improves the computational efficiency by 64% compared to the sequential scheduling. Orbit difference analyses suggest a 10-cm accuracy for the COSMIC orbits from the NRT (NCTU) system, and it is consistent as the NRT University Corporation for Atmospheric Research (URCA) system. The mean velocity accuracy from the NRT orbits of COSMIC is 0.168 mm/s, corresponding to an error of about 0.051 μrad in the bending angle. The rms differences in the NRT COSMIC clock and in GPS clocks between the NRT (NCTU) and the postprocessing products are 3.742 and 1.427 ns. The GPS clocks determined from a partial ground GPS network [from NRT (NCTU)] and a full one [from NRT (UCAR)] result in mean rms frequency stabilities of 6.1E-12 and 2.7E-12, respectively, corresponding to range fluctuations of 5.5 and 2.4 cm and bending angle errors of 3.75 and 1.66 μrad . [ABSTRACT FROM PUBLISHER]

We study an overbooking model for scheduling arrivals at a medical facility under no-show behavior, with patients having different no-show probabilities and different weights. The scheduler has to assign the patients to time slots in such a way that she minimizes the expected weighted sum of the patients' waiting times and the doctor's idle time and overtime. We first consider the static problem, where the set of patients to be scheduled and their characteristics are known in advance. We partially characterize the optimal schedule and introduce a new sequencing rule that schedules patients according to a single index that is a function of their characteristics. Then we apply our theoretical results and conclusions from numerical experiments to sequential scheduling procedures. We propose a heuristic solution to the sequential scheduling problem, where requests for appointments come in gradually over time and the scheduler has to assign each patient to one of the remaining slots that are available in the schedule for a given day. We find that the no-show rate and patients' heterogeneity have a significant impact on the optimal schedule and should be taken under consideration. [ABSTRACT FROM AUTHOR]

Highlights: [•] A multi-project scheduling in critical chain problem is addressed. [•] This paper introduces a multi-objective optimization model. [•] Cloud genetic algorithm is designed to generate priority of multi-project scheduling activities. [Copyright &y& Elsevier]

Abstract: Extract-transform-load (ETL) workflows model the population of enterprise data warehouses with information gathered from a large variety of heterogeneous data sources. ETL workflows are complex design structures that run under strict performance requirements and their optimization is crucial for satisfying business objectives. In this paper, we deal with the problem of scheduling the execution of ETL activities (a.k.a. transformations, tasks, operations), with the goal of minimizing ETL execution time and allocated memory. We investigate the effects of four scheduling policies on different flow structures and configurations and experimentally show that the use of different scheduling policies may improve ETL performance in terms of memory consumption and execution time. First, we examine a simple, fair scheduling policy. Then, we study the pros and cons of two other policies: the first opts for emptying the largest input queue of the flow and the second for activating the operation (a.k.a. activity) with the maximum tuple consumption rate. Finally, we examine a fourth policy that combines the advantages of the latter two in synergy with flow parallelization. [Copyright &y& Elsevier]

Abstract: This paper studies the semi-online scheduling on two uniform machines under a grade of service (GoS) provision, where one machine is available for all jobs and the other one is only available for partial jobs. The objective is to minimize the makespan. We consider three variants, where the optimal makespan, the total size of jobs, and the largest job size are known in advance respectively, and design optimal algorithms for all of them. [Copyright &y& Elsevier]

Highlights: [•] Scheduling sequential tasks in biomass harvesting operations performed by machinery teams. [•] Operational time is more sensitive to changes of machinery team than the operational cost. [•] Balancing decisions between the timeliness of the biomass harvesting with the associate costs. [Copyright &y& Elsevier]

Low-density parity-check (LDPC) codes can be applied in a lot of different scenarios such as video broadcasting and satellite communications. LDPC codes are commonly decoded by an iterative algorithm called belief propagation (BP) over the corresponding Tanner graph. The original BP updates all the variable-nodes simultaneously, followed by all the check-nodes simultaneously as well. We propose a sequential scheduling algorithm based on weighted bit-flipping (WBF) algorithm for the sake of improving the convergence speed. Notoriously, WBF is a low-complexity and simple algorithm. We combine it with BP to obtain advantages of these two algorithms. Flipping function used in WBF is borrowed to determine the priority of scheduling. Simulation results show that it can provide a good tradeoff between FER performance and computation complexity for short-length LDPC codes. [ABSTRACT FROM AUTHOR]

For the global modeling and parameter optimization of manufacturing process featured with multi-extreme quality characteristics, a global sequential design (GSD) approach is proposed to reduce the experimental sample size and improve the predictive performance as well. Firstly, the proposed approach sets up an elementary model of the process using support vector regression (SVR) under a certain kind of initial experiment manner. Secondly, after identifying support vectors of the initial experiment sample, new experiments are added simultaneously at the 45 degree or axial direction of support vectors in each dimension of process parameters. Thereafter a sequential model of the process is set up. Finally, the sequential steps mentioned above are iterated until the lower prediction mean square error (MSE) or the upper sample size acceptable is reached. The simulation and empirical study show that, GSD approach can rationally allocate the experimental runs around the multi-extreme quality characteristics within the whole feasible region of process parameters, which overcome the shortcoming of only finding single extreme using traditional line steepest ascent sequential design and therefore improve the experimental efficiency. Furthermore, compared with uniform grid design, Latin hypercube sampling and uniform design manner, under the same sample size, the prediction MSE of GSD based SVR model decreases 30% at least; while under the same prediction MSE of SVR model, the sample size of GSD decreased 12.0% at least. [ABSTRACT FROM AUTHOR]

The NP-hard problem of scheduling jobs on unrelated parallel machines, in the presence of machine-dependent and sequence-dependent setup times, with the objective of minimizing the makespan, is considered. A variable neighborhood descent search algorithm, hybridized with mathematical programming elements, is presented and its performance is evaluated on a large set of benchmark problem instances. The extensive computational experiments show that the proposed algorithm outperforms previously proposed methods in terms of solution quality as well as computation time. [ABSTRACT FROM AUTHOR]

In this article, the 'information channel diagram' (ICD) approach is introduced as a diagrammatical tool for modelling information flow during the delivery phase of organisations in which goods are deployed or delivered to customers. An initial review and evaluation of current tools for modelling information flow will be conducted based on the characteristics of information flow during the delivery phases in organisations. Diagrammatic primitives and a prescribed modelling methodology for developing an ICD will be presented, and a case scenario of the delivery phase of an organisation within the health care sector will be applied to demonstrate the use of the ICD. The article concludes by discussing some applications, generalisation potential and limitations of the ICD approach. [ABSTRACT FROM AUTHOR]

This paper considers the problem of designing scheduling algorithms for multichannel (e.g., OFDM-based) wireless downlink networks, with a large number of users and proportionally large bandwidth. For this system, while the classical MaxWeight algorithm is known to be throughput-optimal, its buffer-overflow performance is very poor (formally, it is shown that it has zero rate function in our setting). To address this, a class of algorithms called iterated Heaviest matching with Longest Queues First (iHLQF) is proposed. The algorithms in this class are shown to be throughput-optimal for a general class of arrival/channel processes, and also rate-function-optimal (i.e., exponentially small buffer overflow probability) for certain arrival/channel processes. iHLQF, however, has higher complexity than MaxWeight (n^4 versus n^2, respectively). To overcome this issue, a new algorithm called Server-Side Greedy (SSG) is proposed. It is shown that SSG is throughput-optimal, results in a much better per-user buffer overflow performance than the MaxWeight algorithm (positive rate function for certain arrival/channel processes), and has a computational complexity (n^2) that is comparable to the MaxWeight algorithm. Thus, it provides a nice tradeoff between buffer-overflow performance and computational complexity. These results are validated by both analysis and simulations. [ABSTRACT FROM AUTHOR]

Abstract: Hybrid simulation involves the use of multiple simulation paradigms, and is becoming an increasingly common approach to modelling modern, complex systems. Despite growing interest in its use, little guidance exists for modellers regarding the nature and variety of hybrid simulation models. Here, we concentrate on one particular hybrid – that involving agent-based and system dynamics models. Based on an up-to-date review of the literature, we propose three basic types of hybrid agent-based system dynamics simulations, referred to here as interfaced, integrated and sequential hybrid designs. We speculate that the classification presented may also be useful for other classes of hybrid simulations. [Copyright &y& Elsevier]

Metaheuristics are solution methods which combine local improvement procedures and higher level strategies for solving combinatorial and nonlinear optimization problems. In general, metaheuristics require an important amount of effort focused on parameter setting to improve its performance. In this work a 2-level metaheuristic approach is proposed so that Scatter Search and Ant Colony Optimization act as "low level" metaheuristics, whose parameters are set by a "higher level" Genetic Algorithm during execution, seeking to improve the performance and to reduce the maintenance. The Set Covering Problem is taken as reference since is one of the most important optimization problems, serving as basis for facility location problems, airline crew scheduling, nurse scheduling, and resource allocation [ABSTRACT FROM AUTHOR]