Revised multi-choice goal programming for multi-period,multi-stage inventory controlled supply chain model with popup stores in Guerrilla marketing

In this paper, we consider a supply chain network design problem with popup stores which can be opened for a few weeks or months before closing seasonally in a marketplace. The proposed model is multi-period and multi-stage with multi-choice goals under inventory management constraints and formulated by 0–1 mixed integer linear programming. The design tasks of the problem involve the choice of the popup stores to be opened and the distribution network design to satisfy the demand with three multi-choice goals. The first goal is minimization of the sum of transportation costs in all stages; the second is to minimization of set up costs of popup stores; and the third goal is minimization of inventory holding and backordering costs. Revised multi-choice goal programming approach is applied to solve this mixed integer linear programming model. Also, we provide a real-world industrial case to demonstrate how the proposed model works.     

基于近红外光谱技术的石油组分定量分析新方法

针对石油化工产品生产控制和质量检查的需要,为提高测定产品组成的效率,将近红外光谱法作为基础测定方法,以直馏柴油、加氢精制柴油和催化裂化柴油为校正模型的训练样本,测定其中饱和烃、胶质、单环芳烃、双环芳烃、三环芳烃和环烷烃的组成,论述了采用模糊神经网络建立校正模型测定石油化工产品组成的可行性。基于dSPACE硬件平台,用验证样本对模糊神经网络校正模型进行了检验,实验结果表明,该方法响应快、误差小、鲁棒性强,在近红外长波区内,校正样品和验证样品的均方误差小于10-6。该方法可用于石油化工产品的生产工艺研究中。     

Reliable computing with unreliable components: Using separable environments to stabilize long-term information storage

How, in the face of both intrinsic and extrinsic volatility, can unconventional computing fabrics store information over arbitrarily long periods? Here, we argue that the predictable structure of many realistic environments, both natural and artificial, can be used to maintain useful categorical boundaries even when the computational fabric itself is inherently volatile and the inputs and outputs are partially stochastic. As a concrete example, we consider the storage of binary classifications in connectionist networks, although the underlying principles should be applicable to other unconventional computing paradigms. Specifically, we demonstrate that an unsupervised, activity dependent plasticity rule, AHAH (Anti-Hebbian-And-Hebbian), allows binary classifications to remain stable even when the underlying synaptic weights are subject to random noise. When embedded in environments composed of separable features, the weight vector is restricted by the AHAH rule to local attractors representing stable partitions of the input space, allowing unsupervised recovery of stored binary classifications following random perturbations that leave the system in the same basin of attraction. We conclude that the stability of long-term memories may depend not so much on the reliability of the underlying substrate, but rather on the reproducible structure of the environment itself, suggesting a new paradigm for reliable computing with unreliable components.     

Characterizing the influence of reinforcing resin on the structure and the mechanical response of filled isoprene rubber

In the present study, a specific type of reinforcing resin behavior is investigated using various mechanical approaches as well as microscopic techniques such as transmission electron microscopy and atomic force microscopy. Based on these observations, it could be concluded that the reinforcing resin introduces a synergistic effect with carbon black in order to strengthen the system. This is implied since the percolation threshold is significantly reduced and the morphology of the filler aggregates changes toward higher compactness, i.e., an increase of volume-to-size ratio with the addition of resin.     

Design and build an elastic crosslinked network to strengthen and toughen soybean-meal based bioadhesive using organo-sepiolite and greener crosslinker triglycidylamine

Poor water resistance and curing layer brittleness are significant challenges of greener soybean meal (SM) adhesive promotion and application. In this study an elastic network was built to crosslink the SM adhesive and improve the cured layer toughness of the resultant adhesive. Long-chain organo-sepiolite (OSEP) was first prepared by compound modification using KH-560 and KH-602 silane coupling agents. Triglycidylamine (TGA), a greener crosslinking agent with a large number of effective epoxy functional groups, was then synthesized. SM-based adhesives were fabricated using SM, OSEP and TGA. Results indicated that an elastic network was fabricated by the cross-linked reaction of SM, OSEP, and TGA. The elastic network effectively improved the toughness of the resultant adhesive. The wet shear strength of the plywood (PlyW) bonded by SM with 1% OSEP (SM/TGA/OSEP-1 adhesive) increased by 60% to 1.25 MPa relative to that of the SM/TGA adhesive. This study provided a greener, simple and cheap method improving the toughness and wet shear strength of SM-based adhesive, which contributes to the industrialized application of the product.     

Algebro-geometric Constructions of Quasi Periodic Flows of the Discrete Self-dual Network Hierarchy and Applicationsℰ

In this paper we obtain the discrete integrable self-dual network hierarchy associated with a discrete spectral problem. On the basis of the theory of algebraic curves, the continuous flow and discrete flow related to the discrete self-dual network hierarchy are straightened using the Abel-Jacobi coordinates. The meromorphic function and the Baker-Akhiezer function are introduced on the hyperelliptic curve. Quasi-periodic solutions of the discrete self-dual network hierarchy are constructed with the help of the asymptotic properties and the algebra-geometric characters of the meromorphic function, the Baker-Akhiezer function and the hyperelliptic curve.     

Nonlinear Time Series Forecast Using Radial Basis Function Neural Networks

In the research of using Radial Basis Function Neural Network (RBF NN) forecasting nonlinear time series, we investigate how the different clusterings affect the process of learning and forecasting. We find that k-means clustering is very suitable. In order to increase the precision we introduce a nonlinear feedback term to escape from the local minima of energy, then we use the model to forecast the nonlinear time series which are produced by Mackey-Glass equation and stocks. By selecting the k-means clustering and the suitable feedback term, much better forecasting results are obtained.     

A hybrid genetic algorithm–neural network strategy for simulation optimization

Simulation optimization aims at determining the best values of input parameters, while the analytical objective function and constraints are not explicitly known in terms of design variables and their values only can be estimated by complicated analysis or time-consuming simulation. In this paper, a hybrid genetic algorithm–neural network strategy (GA–NN) is proposed for such kind of optimization problems. The good approximation performance of neural network (NN) and the effective and robust evolutionary searching ability of genetic algorithm (GA) are applied in hybrid sense, where NNs are employed in predicting the objective value, and GA is adopted in searching optimal designs based on the predicted fitness values. Numerical simulation results and comparisons based on a well-known pressure vessel design problem demonstrate the feasibility and effectiveness of the framework, and much better results are achieved than some existed literature results.     

Performance analysis of optical wavelength switching network using multiple output light source

In this paper a 32×32 optical wavelength switching network using the multi-channel and multi-wavelength spectrum-sliced light source is presented where the 2×2 optical-switching elements are utilized in dilated Benes and modified dilated Benes configurations, respectively. The system performances in bit-error rate, especially for the effects of intraband crosstalk, are analyzed in details for the two type networks. Through the simulation studies, the dilated Benes shows better performances than the modified dilated Benes configuration.     

PLS分析与RBF神经网络耦合环境模型

鉴于城市大气环境质量受到诸多复杂因素影响,且各因素间存在多重相关性,本文将偏最小二乘(PLS)分析与人工神经网络径向基网络(RBF)耦合,建立偏最小二乘径向基神经网络模型(PLSRBF),应用于贵阳大气环境质量的检验和预测。实例表明:PLSRBF模型可对原多自变量模型进行降维简化,并可有效提取解释变量信息,防止信息丢失,且具有较强的拟合能力。