Indian Institute of Technology Bhubaneswar

About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Computer science. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

A comparative performance assessment of a set of multiobjective algorithms for constrained portfolio assets selection

Abstract: This paper addresses a realistic portfolio assets selection problem as a multiobjective optimization one, considering the budget, floor, ceiling and cardinality as constraints. A novel multiobjective optimization algorithm, namely the non-dominated sorting multiobjective particle swarm optimization (NS-MOPSO), has been proposed and employed efficiently to solve this important problem. The performance of the proposed algorithm is compared with four multiobjective evolution algorithms (MOEAs), based on non-dominated sorting, and one MOEA algorithm based on decomposition (MOEA/D). The performance results obtained from the study are also compared with those of single objective evolutionary algorithms, such as the genetic algorithm (GA), tabu search (TS), simulated annealing (SA) and particle swarm optimization (PSO). The comparisons of the performance include three error measures, four performance metrics, the Pareto front and computational time. A nonparametric statistical analysis, using the Sign test and Wilcoxon signed rank test, is also performed, to demonstrate the superiority of the NS-MOPSO algorithm. On examining the performance metrics, it is observed that the proposed NS-MOPSO approach is capable of identifying good Pareto solutions, maintaining adequate diversity. The proposed algorithm is also applied to different cardinality constraint conditions, for six different market indices, such as the Hang-Seng in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA, Nikkei 225 in Japan, and BSE-500 in India.

Modeling Post-disaster Challenges of Humanitarian Supply Chains: A TISM Approach

Abstract: Concern related to risk reduction and sustainability in disaster relief aid is increasing from past few years and will be continued due to increasing frequency of disasters and the people coming in their ways. The approach of any disaster relief activities must not be limited only to saving lives and providing relief but also to making the communities capable for next disasters they may face. Relief aid distribution is usually logistics activites but unpredictable nature of disaster, damaged communication systems, asymmetry in relief-demand information and a chaotic environment created by the disaster make it as the most challenging. Therefore, it appeals the need to identify various challenges of humanitarian supply chains and to develop a hierarchy towards achieving sustainability in relief during post extreme events. In this article, total interpretive structural modelling, an interactive tool and an innovative version of interpretive structural modeling technique, has been applied as the methodology to develop a hierarchical model that also explains the interrelation of selected fifteen challenges and the level of the hierarchy. Result of the developed model suggests that the poor strategic planning and lack of investment in ex-ante measures (limited numbers and capacity of shelters, and less numbers of in-country warehouses) lead to the lack of sustainable relief aid.

Data-Mining-Based Intelligent Differential Relaying for Transmission Lines Including UPFC and Wind Farms

Abstract: This paper presents a data-mining-based intelligent differential relaying scheme for transmission lines, including flexible ac transmission system device, such as unified power flow controller (UPFC) and wind farms. Initially, the current and voltage signals are processed through extended Kalman filter phasor measurement unit for phasor estimation, and 21 potential features are computed at both ends of the line. Once the features are extracted at both ends, the corresponding differential features are derived. These differential features are fed to a data-mining model known as decision tree (DT) to provide the final relaying decision. The proposed technique has been extensively tested for single-circuit transmission line, including UPFC and wind farms with in-feed, double-circuit line with UPFC on one line and wind farm as one of the substations with wide variations in operating parameters. The test results obtained from simulation as well as in real-time digital simulator testing indicate that the DT-based intelligent differential relaying scheme is highly reliable and accurate with a response time of 2.25 cycles from the fault inception.