Sensitivity Analysis of Transportation Production Costs in Indonesia

The transportation production cost (TPC) considerably has strong influence to the national economic condition. This paper focused on the analysis of the transportation production cost sensitivity in relation to the variation of the external affecting factor, which are fuel price, rupiah exchange rate and Bank of Indonesia interest rate. Based on the R2 values, the TPC components in general have significant correlation, with the fuel prices. However, they do not have high correlation to the fluctuation of interest rate and rupiah exchange rate. The sensitivity analysis shows that a 10% rise on fuel price would cause 6%, 2%, 7%, 2.4%, and 4.9% rise on the TPC of intercity bus, ferry ship, interisland ship, train, and airline, respectively

ICAN sensitivity analysis

A computer program called Integrated Composite Analyzer (ICAN) was used to predict the properties of high-temperature polymer matrix composites. ICAN is a collection of NASA Lewis Research Center-developed computer codes designed to carry out analysis of multilayered fiber composites. The material properties used as input to the program were those of the thermoset polyimide resin PMR-15 and the carbon fiber Celion 6000. The sensitivity of the predicted composite properties to variations in the resin and fiber properties was examined. In addition, the predicted results were compared with experimental data. In most cases, the effect of changes in resin and fiber properties on composite properties was reasonable. However, the variations in the composite strengths with the moisture content of the PMR-15 resin were inconsistent. The ICAN-predicted composite moduli agreed fairly well with experimental values, but the predicted composite strengths were generally lower than experimental values

Sensitivity Analysis of Flexible Provisioning

This technical report contains a sensitivity analysis to extend our previous work. We show that our flexible service provisioning strategy is robust to inaccurate performance information (when the available information is within 10% of the true value), and that it degrades gracefully as the information becomes less accurate. We also identify and discuss one particular case where inaccurate information may lead to undesirable losses in highly unreliable environments

Sensitivity analysis for network aggregative games

We investigate the sensitivity of the Nash equilibrium of constrained network aggregative games to changes in exogenous parameters affecting the cost function of the players. This setting is motivated by two applications. The first is the analysis of interventions by a social planner with a networked objective function while the second is network routing games with atomic players and information constraints. By exploiting a primal reformulation of a sensitivity analysis result for variational inequalities, we provide a characterization of the sensitivity of the Nash equilibrium that depends on primal variables only. To derive this result we assume strong monotonicity of the mapping associated with the game. As the second main result, we derive sufficient conditions that guarantee this strong monotonicity property in network aggregative games. These two characterizations allows us to systematically study changes in the Nash equilibrium due to perturbations or parameter variations in the two applications mentioned above

Validation and sensitivity analysis of InfoCrop simulation model for growth and yield of Indian mustard varieties at Allahabad

Field experiment was carried out at SHUATS, Allahabad, to study validation and sensitivity analysis of InfoCrop model with the data sets generated respectively during Rabi season of 2016-17. The main plot treatments and sub-plot treatment consisted three dates of sowing and cultivars (D1-25th October, D2-5th November and D3-15th November) and (V1- Parasmani, V2- Varuna and V3- SRM 777) using split plot design. The results revealed that simulation of growth and yield parameters were compared with observed data and results concluded that the model overestimates all the parameters within the acceptable range