Genotype By Environment Interaction and Stability Analysis for Maize Hybrids in North Western Himalayas Ecology

Genotype (G) x Environment (E) interaction of 25 medium maturity maize hybrids tested at three environments inNorth-Western Himalayas was analyzed to identify stable high yielding hybrids for mid-hill conditions. The G x Einteraction was studied using different stability statistics viz; Additive main effects and multiplicative interaction(AMMI), AMMI stability value (ASV), rank-sum (RS), and yield stability index (YSI). Combined analysis of varianceshows that genotype, environment, and G x E interaction is highly significant. This indicated the possibility of selectionof stable genotypes across the environments. The results of the AMMI analysis showed that the first two principalcomponents (PC1-PC2) were highly significant (P<0.05). The partitioning of TSS (total sum of squares) exhibitedthat the environment effect was a predominant source of variation followed by genotypes and G×E interaction,suggesting the possible existence of different environmental groups. The first two interaction principal componentaxis (IPCA) cumulatively explained 82.87% of the total interaction effect. The study revealed that G11 and G7 werefound to be stable based on all stability statistics and GGE biplot assessment. Based on GGE biplots, it is concludedthat E3 is the best environment for testing the hybrids for more extensive adaptability and E2 and E1 locations can beused to identify location-specific hybrids. Grain yield is positively and significantly correlated with rank-sum (RS)And yield stability index (YSI). The above-mentioned stability statistics could be useful for identification of stablehigh yielding genotypes, whereas, GGE biplots facilitated visual comparisons of high yielding genotypes acrossthe multi-environments

Computational Study for Disrupted Production System with Time Dependent Demand

294-301A production system often gets disrupted due to uncertain and un-planned events like labor problem, inadequate manufacturing, machine breakdown, power supply failure etc. and manufacturer fails to deliver the product to retailers well in time. This may reduce the reliability and faith of customers on the company/product. So, manufacturer needs to study the variation of demand and customer arrival pattern before the system gets disrupted and readjust the reproduction time according to disruption. A flexible managerial decision policy for disruption based production system is required at this juncture. The paper considers the same and incorporates variable demand with the constant production rate. We have solved the disruption problem analytically to determine the production period before and after disruptions. Both increasing and decreasing trend in production run has been studied for deteriorating item. A numerical example and simulation study appended for sensitivity analysis in order to find which parameter is responsible for significant changes in disrupted production system