Discrete particle swarm optimization based on estimation of distribution for terminal assignment problems

Terminal assignment problem (TEAP) is to determine minimum cost links to form a network by connecting a given set of terminals to a given collection of concentrators. This paper presents a novel discrete particle swarm optimization (PSO) based on estimation of distribution (EDA), named DPSO-EDA, for TEAP. EDAs sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The DPSO-EDA incorporates the global statistical information collected from personal best solutions of all particles into the PSO, and therefore each particle has comprehensive learning and search ability. In the DPSO-EDA, a modified constraint handling method based on Hopfield neural network (HNN) is also introduced to fit nicely into the framework of the PSO and thus utilize the merit of the PSO. The DPSO-EDA adopts the asynchronous updating scheme. Further, the DPSO-EDA is applied to a problem directly related to TEAP, the task assignment problem (TAAP), in order to show that the DPSO-EDA can be generalized to other related combinatorial optimization problems. Simulation results on several problem instances show that the DPSO-EDA is better than previous methods.     

Process and learning outcomes from remotely-operated,simulated, and hands-on student laboratories

A large-scale, multi-year, randomized study compared learning activities and outcomes for hands-on, remotely-operated, and simulation-based educational laboratories in an undergraduate engineering course. Students (N = 458) worked in small-group lab teams to perform two experiments involving stress on a cantilever beam. Each team conducted the experiments in one of three lab formats (hands-on, remotely-operated, or simulation-based), collecting data either individually or as a team. Lab format and data-collection mode showed an interaction, such that for the hands-on lab format learning outcomes were higher when the lab team collected data sets working as a group rather than individually collecting data sets to be combined later, while for remotely-operated labs individual data collection was best. The pattern of time spent on various lab-related activities suggests that working with real instead of simulated data may induce higher levels of motivation. The results also suggest that learning with computer-mediated technologies can be improved by careful design and coordination of group and individual activities.     

Effect of animation enhanced conceptual change texts on 6th grade students’ understanding of the particulate nature of matter and transformation during phase changes

In this study, the effect of animation enhanced conceptual change texts (CCT–CA) on grade 6 students’ understanding of the particulate nature of matter (PNM) and transformation during the phase changes was investigated. A quasi-experimental design and one control group (CG, N = 25) and one experimental group (EG, N = 26) were used. While the control group taught traditional instruction, the experimental group received CCT–CA instruction. Two different tests, The Particulate Nature of Matter Concept Test (ParNoMaC) and The Transformation of Matter Statement Test (ToMaSaT), were administered as pretest, posttest and delayed test to collect data. Results indicate that while there is no statistically significant difference between groups in pretest, performance of EG students is greater than the CG ones in posttest and delayed test. And also, the EG students are better in remediating their alternative conceptions related to the PNM and transformations during the phase changes. Based on the study, it is concluded that CCT–CA combination may be effective way to improve students’ understanding of basic science and chemical concepts.     

Modeling of thermodynamic properties of ABr-PrBr3 (A=Li-Cs ) systems

The thermodynamic properties of ABr-PrBr₃(A=Li-Cs) systems were assessed by the CALPHAD method. The liquid phase in the systems was described by the non-stoichiometric associate model. The entropies of mixing in the liquid were evaluated from experimental liquidus and enthalpy of mixing data. For the pseudobinary compounds A₃PrBr₆,APr₂Br₇, and A₂PrBr₅ (A=K,Rb) and Cs₃PrBr₆ and CsPr₂Br₇, the dependences of Gibbs energies of formation on temperature were calculated. The anomalies of sequences of thermodynamic properties in RbBr-PrBr₃ were observed and discussed. The nature of the liquid phase and precision of calculations of the Rb₂PrBr₅(s) compound were discussed.     

Product structure (BOM)-based product similarity measures using orthogonal procrustes approach

In geometry, two configurations are called similar if they can be brought to a complete match by rigid motions and dilations. In the simplest case, product designs X and Y have the same dimensionality and same number of points which can be brought into a one to one correspondence by substantive considerations. Under orthogonal transformations, product design Y can only be rotated and reflected in order to approximate product design X. As for the “as close as possible” or “relatively close” criterion, a reasonable definition would be to measure the distances between corresponding points, square these values, and add them to obtain the sum-of-squares criterion L. In this paper, a product structure (BOM)-based product similarity measures using orthogonal procrustes approach is proposed. A structural product is first transformed into bill of materials (BOM), the structural similarity measurement between a product query and target product is calculated and evaluated using orthogonal procrustes approach. The results show that the proposed product structure similarity measure method can help companies find desired product information based on BOM feature.     

Comparison of experimental designs for simulation-based symbolic regression of manufacturing systems

In this article, an empirical analysis of experimental design approaches in simulation-based metamodelling of manufacturing systems with genetic programming (GP) is presented. An advantage of using GP is that prior assumptions on the structure of the metamodels are not required. On the other hand, having an unknown structure necessitates an analysis of the experimental design techniques used to sample the problem domain and capture its characteristics. Therefore, the study presents an empirical analysis of experimental design methods while developing GP metamodels to predict throughput rates in a common industrial system, serial production lines. The objective is to identify a robust sampling approach suitable for GP in simulation-based metamodelling. Experiments on different sizes of production lines are presented to demonstrate the effects of the experimental designs on the complexity and quality of approximations as well as their variance. The analysis showed that GP delivered system-wide metamodels with good predictive characteristics even with the limited sample data.     

A two-stage stochastic programming model for the parallel machine scheduling problem with machine capacity

This paper proposes a two-stage stochastic programming model for the parallel machine scheduling problem where the objective is to determine the machines' capacities that maximize the expected net profit of on-time jobs when the due dates are uncertain. The stochastic model decomposes the problem into two stages: The first (FS) determines the optimal capacities of the machines whereas the second (SS) computes an estimate of the expected profit of the on-time jobs for given machines' capacities. For a given sample of due dates, SS reduces to the deterministic parallel weighted number of on-time jobs problem which can be solved using the efficient branch and bound of M’Hallah and Bulfin [16]. FS is tackled using a sample average approximation (SAA) sampling approach which iteratively solves the problem for a number of random samples of due dates. SAA converges to the optimum in the expected sense as the sample size increases. In this implementation, SAA applies a ranking and selection procedure to obtain a good estimate of the expected profit with a reduced number of random samples. Extensive computational experiments show the efficacy of the stochastic model.     

Static timing analysis for modeling QoS in networks-on-chip

Networks-on-chip (NoCs) are used in a growing number of SoCs and multi-core processors. Because messages compete for the NoC’s shared resources, quality of service and resource allocation are major concerns for system designers. In particular, a model for the properties of packet delivery through the network is desirable. We present a methodology for packet-level static timing analysis in NoCs. Our methodology quickly and accurately gauges the performance parameters of a virtual-channel wormhole NoC without simulation. The network model can handle any topology, link capacities, and buffer sizes. It provides per-flow delay analysis that is orders-of-magnitude faster than simulation while being significantly more accurate than prior static modeling techniques. Using a carefully derived and reduced Markov chain, the model can statically represent the dynamic network state. Usage of the model in a placement optimization problem is shown as an example application.     

Multilevel augmentation algorithms based on fast collocation methods for solving ill-posed integral equations

This paper continues the theme of the recent work Chen et al. (2008) [18], in which fast collocation methods are introduced for solving ill-posed Fredholm integral equations of the first kind. We develop in this paper multilevel augmentation algorithms, which lead to fast solutions of the discrete equations resulting from fast collocation methods. Regularization parameter choice strategies are given for proposed methods. The theoretical analysis and numerical experiments illustrate the accuracy and efficiency of the algorithm.