Dariush Khezrimotlagh

An integrated model for green supplier selection under fuzzy environment: application of data envelopment analysis and genetic programming approach

Supplier evaluation plays a critical role in a successful supply chain management. Hence, the evaluation and selection of the right suppliers have become a central decision of manufacturing business activities around the world. Consequently, numerous individual and integrated methods have been presented to evaluate and select suppliers. The current literature shows that hybrid artificial intelligence (AI)-based models have received much attention for supplier evaluation. Integrated data envelopment analysis–artificial neural network (DEA–ANN) is one of the combined methods that have recently garnered great attention from academics and practitioners. However, DEA–ANN model has some drawbacks, which make some limitation in the evaluation process. In this study, we aim at improving the previous DEA–AI models by integrating the Kourosh and Arash method as a robust model of DEA with a new AI approach namely genetic programming (GP) to overcome the shortcomings of previous DEA–AI models in supplier selection. Indeed, in this paper, GP provides a robust nonlinear mathematical equation for the suppliers’ efficiency using the determined criteria. To validate the model, adaptive neuro-fuzzy inference system as a powerful tool was used to compare the result with GP-based model. In addition, parametric analysis and unseen data set were used to validate the precision of the model.

A New Method for Evaluating Decision Making Units in DEA

This paper provides a new structure in data envelopment analysis (DEA) for assessing the performance of decision making units (DMUs). It proposes a technique to estimate the DEA efficient frontier based on the Arash Method in a way different from the statistical inferences. The technique allows decisions in the target regions instead of points to benchmark DMUs without requiring any more information in the case of interval/fuzzy DEA methods. It suggests three efficiency indexes, called the lowest, technical and highest efficiency scores, for each DMU where small errors occur in both input and output components of the Farrell frontier, even if the data are accurate. These efficiency indexes provide a sensitivity index for each DMU and arrange both inefficient and technically efficient DMUs together while simultaneously detecting and benchmarking outliers. Two numerical examples depicted the validity of the proposed method.

MATIN: a random network coding based framework for high quality peer-to-peer live video streaming.

In recent years, Random Network Coding (RNC) has emerged as a promising solution for efficient Peer-to-Peer (P2P) video multicasting over the Internet. This probably refers to this fact that RNC noticeably increases the error resiliency and throughput of the network. However, high transmission overhead arising from sending large coefficients vector as header has been the most important challenge of the RNC. Moreover, due to employing the Gauss-Jordan elimination method, considerable computational complexity can be imposed on peers in decoding the encoded blocks and checking linear dependency among the coefficients vectors. In order to address these challenges, this study introduces MATIN which is a random network coding based framework for efficient P2P video streaming. The MATIN includes a novel coefficients matrix generation method so that there is no linear dependency in the generated coefficients matrix. Using the proposed framework, each peer encapsulates one instead of n coefficients entries into the generated encoded packet which results in very low transmission overhead. It is also possible to obtain the inverted coefficients matrix using a bit number of simple arithmetic operations. In this regard, peers sustain very low computational complexities. As a result, the MATIN permits random network coding to be more efficient in P2P video streaming systems. The results obtained from simulation using OMNET++ show that it substantially outperforms the RNC which uses the Gauss-Jordan elimination method by providing better video quality on peers in terms of the four important performance metrics including video distortion, dependency distortion, End-to-End delay and Initial Startup delay.

Efficient P2P Live Video Streaming Over Hybrid WMNs Using Random Network Coding

As random network coding (RNC) considerably increases the network throughput, it has been of great interest for video streaming over wireless mesh networks (WMNs). However, mobile video users suffer from high transmission overhead due to the transmission of large coefficient vectors as headers and an excessive imposed decoding computational complexity due to using the Gauss–Jordan elimination method in RNC. This complexity cannot be supported by the embedded mobile processors. To overcome these limitations, this study analyses the impact of applying a method that simplifies RNC requirements on WMNs. This method is based on the generation of a full rank coefficients matrix without any linear dependency among its vectors. Nodes encapsulate one instead of

Data Envelopment Analysis

n

Possibility and Practicability

n coefficients entries into a packet which leads to very low transmission overhead. Receivers can obtain the inverted coefficients matrix by performing very few arithmetic operations. Consequently, wireless nodes experience very low decoding computational complexity eliminating the need for powerful processors and high battery energy sources. The wireless medium is also less occupied and the transmission processes are shorter. Simulation results in the OMNeT++ framework depict that the applied method provides high video quality on the nodes by addressing the mentioned challenges, even if high mobility rates exist in the WMN.

The Optimization Approach

This note shows that the input targets of proposed model by Kuosmanen and Kazemi Matin (2009) (http://dx.doi.org/10.1016/j.ejor.2007.09.040 and http://dx.doi.org/10.1016/j.omega.2008.11.002) may not be less than the input targets of proposed model by Lozano and Villa (2006) (http://dx.doi.org/10.1016/j.cor.2005.02.031).

The Delta Neighborhood

The outcomes from the previous chapters provide useful information from the literature of operations research and economics on measuring the performance of a set of homogenous firms with multiple input factors and multiple output factors as well as ranking and benchmarking firms. If firms are not homogenous, the situation is the same as when each factor has a different unit of measurement from one firm to another, and therefore, no meaningful discrimination can be expressed, unless the simple conditions of discrimination, which are represented in Sect. 2.3, are satisfied. In this chapter, the concepts introduced in the previous chapters are adapted with the literature and the philosophical background is discussed.