Scientia Iranica
Volume:23 Issue: 2, 2016

The earned Value Management (EVM) is a project management technique used to measure project progress by integrating management efficiently of the three most important elements in a project; cost, schedule and scope. This paper presents an evidential reasoning (ER) based model for estimating the Earned Value (EV) of the projects activities with uncertainties in progress data. Since that subjective nature of EV measurement can incorporate into errors and uncertainties which cause biased judgments; and as the uncertainty is inherent in real-life activities, the developed ER based model is very useful to evaluate the EV of a project where uncertainty arises. A case study is provided to illustrate how the new model will be used and can be implemented in reality.

In this paper a multi-echelon location-distribution problem is modeled considering customer priorities.A lexicographic approach is implemented to determine the most preferred distribution path according to the customers’ priorities.Predetermined number of trucks moved from depots and satellitesis considered in the proposed model. The results show that the proposed approach can better consider the customers with different priorities while more important customers will have less total costs compared tothe classic approach. Moreover, the sensitivity analysis has been donefor discovering of related parameters effects in the model.

In this study, we develop a neural network with a time shifting approach to forecast time series patterns. We investigate the impact of different layer-weight configurations to capture the trends in the forms of seasonal, chaotic, etc. We also hypothesize the combined effect of the delayed inputs and the forward connections to introduce a dynamical structure. The effect of overfitting issue is procedurally monitored to gain the resistance property from the early stoppage of training process and to reduce the predictions'' error. Finally, the performance of the proposed network is challenged by six well-known deterministic and non-deterministic time series and compared by the autoregression (AR), artificial neural network (ANN), adaptive k-nearest neighbors (AKN), and adaptive neural network (ADNN) models. The results show that the proposed network outperforms the conventional models, particularly in forecasting the chaotic and seasonal time series.

This paper proposes a robust optimization model for robust and reliable design of an integrated forward-reverse logistics network with hybrid facilities under uncertainty and random facility disruptions. The proposed model utilizes several effective reliability strategies to mitigate the impact of random facility disruptions. First, the proposed model allows two types of hybrid facilities, namely, reliable and unreliable, to be located in the concerned logistics network where unreliable ones may be partially or fully disrupted, and thus a percentage of their capacities may be lost. However, they can still serve their customers with remaining of their available capacities. Furthermore, a sharing strategy is taken into account, in which goods can be shipped from reliable hybrid facilities to unreliable ones to compensate their lost capacity. A robust optimization approach is applied on the developed model to handle the uncertainties in the parameters of the concerned network. Finally, several numerical experiments along with a sensitivity analysis are conducted to illustrate the significance and applicability of the proposed model as well as the effectiveness of the robust optimization approach in this context.

The classical Economic Production Quantity (EPQ) inventory model does not consider ordering and holding costs of raw materials. In this direction, this paper considers the ordering and holding costs for both raw materials and nished product. Basically, four EPQ inventory models are developed from an easy perspective that has not been considered before. It was found that the ordering and holding costs of raw materials must be taken into account, because they signi cantly impact on the optimal production lot size of the nished product in both EPQ without shortages and EPQ with shortages inventory models. Furthermore, an EPQ inventory model that determines the optimal lot size for a product that requires more than one raw material, and an EPQ inventory model that obtains the optimal batch size for multiple products, which are manufactured with multiple raw materials, are proposed. Numerical examples are presented in order to illustrate the use of the proposed inventory models.

Choice-based network revenue management concentrates on importing choice models within the traditional revenue management system. Multinomial logit is a popular and well-known model which is base choice model in the revenue management. Empirical results indicate inadequacy of this model for predicting itinerary shares and more realistic models such as nested logit can be proposed for substituting it. Incorporating complex choice models in the optimization module based on statistical tests without considering the complexity of the obtained mathematical model, would lead to increase the complexity of a system without obtaining significant improvement. According to influencing the discrete choice model on the structure of optimization model, it is necessary to analyze the interaction between specific discrete choice and optimization models.In this paper, a knowledge acquisition subsystem is introduced for providing intelligence and considering the most suitable choice models. We develop the feedforward multilayer perceptron artificial neural network for forecasting revenue improvement percent obtained by using more realistic choice models. The obtained results demonstrate new system will decrease the complexity of the system simultaneously with preserving the firm’s revenue. According to the computational results, by increasing the resource restriction, the process of incorporating more realistic choice model will be more important.

In this paper, we focus specifically on a two stage process with multivariate-attribute quality characteristics in the second stage. The main purpose of this study is extending discriminant analysis (DA) based control charts to monitor a two stage process. We propose three methods including EWMA (DA), integrated EWMA (DA) and P-value (DA), and integrated multivariate exponentially weighted moving average (MEWMA) and T2 charts based on the DA approach to monitor the multivariate-attribute quality characteristic in a two stage process. The performance of the proposed methods is evaluated through simulation studies as well as a real case.