Order Selection and Capacity Planning in MCS with Chain-to-Chain Collaboration

Due to rejecting order in a single supply chain for lack of adequate capacity, a multi-chain system is introduced to avoid this potential operational risk. Based on four categories of order： direct order, reserve order, chain-to-chain order and rejected order, the framework of order selection in multi-chain system（MCS） is presented, and the model of order selection and planning under chain-to-chain collaboration is formulated. Then, the Lagrange algorithm is used to solve this problem through Lagrange relaxation and decomposition. Finally, numerical study show that opportunity cost of rejecting reserve order and production cost of chain-to-chain order have significant impacts on order selection, and there exists a critical threshold value of the combination of two factors. Through the combination, the multi-chain system can obtain the optimal status, meanwhile manager can utilize this to realize different strategies in MCS.

Adaptive load forecasting of the Hellenic electric grid

Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid's utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal be-havior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.

A Theory of the Risk for Empirical CVaR with Application to Portfolio Selection

When decisions are based on empirical observations,a trade-off arises between flexibility of the decision and ability to generalize to new situations.In this paper,we focus on decisions that are obtained by the empirical minimization of the Conditional Value-at-Risk(CVa R)and argue that in CVa R the trade-off between flexibility and generalization can be understood on the ground of theoretical results under very general assumptions on the system that generates the observations.The results have implications on topics related to order and structure selection in various applications where the CVa R risk-measure is used.A study on a portfolio optimization problem with real data demonstrates our results.

Study on joint Bayesian model selection and parameter estimation method of GTD model

The Bayesian method is applied to the joint model selection and parameter estimation problem of the GTD model. An algorithm based on RJ-MCMC is designed. This algorithm not only improves the model order selection and parameter estimation accuracy by exploiting the priori information of the GTD model, but also solves the mixed parameter estimation problem of the GTD model properly. Its performance is tested using numerical simulations and data generated by electromagnetic code. It is shown that it gives good model order selection and parameter estimation results, especially for low SNR, closely-spaced components and short data situations.

Science Letters:Average SNR of maximum ratio transmission with selection combining

Two kinds of selection combining schemes including generalized selection combining (GSC) and generalized order selection combining (GOSC) are investigated. In the GSC scheme, L strongest diversity branches from a total of R diversity branches are selected and coherently combined by maximal ratio combining. GOSC means that the Lth strongest diversity branch from R diversity branches is selected for reception. Closed-form expressions for the average signal-to-noise ratios of maximum ratio transmission with GSC and GOSC are derived in Rayleigh fading channels.

Smoothing Non-Stationary Time Series Using the Discrete Cosine Transform

This paper considers the problem of smoothing a non-stationary time series(having either deterministic and/or stochastic trends) using the discrete cosine transform(DCT).The DCT is a powerful tool which has found fruitful applications in filtering and smoothing as it can closely approximate the optimal Karhunen-Loeve transform(KLT).In fact,it is known that it almost corresponds to the KLT for first-order autoregressive processes with a root close to unity:This is the case with most economic and financial time series.A number of new results are derived in the paper:(a) The explicit form of the linear smoother based on the DCT,which is found to have time-varying weights and that uses all observations;(b) the extrapolation of the DCT-smoothed series;(c) the form of the average frequency response function,which is shown to approximate the frequency response of the ideal low pass filter;(d) the asymptotic distribution of the DCT coefficients under the assumptions of deterministic or stochastic trends;(e) two news method for selecting an appropriate degree of smoothing,in general and under the assumptions in(d).These findings are applied and illustrated using several real world economic and financial time series.The results indicate that the DCT-based smoother that is proposed can find many useful applications in economic and financial time series.