Impact Of Various Decisions Research Articles

Evaluating the Financial Future of Electric Utilities Using Scenarios Analysis: The Case of Tamil Nadu

This paper analyzes the impact of multiple scenarios on the utility’s financials using Revenue and Tariff Analysis for Electric Utilities (RATE) model. The RATE modeling tool can provide actionable insights into the impact of various decisions on overall demand–supply scenarios, the energy balance, and revenue gaps. The model was customized for application to Tamil Nadu (TN) by World Resources Institute (WRI) in collaboration with Prayas (Energy Group) as a knowledge partner.

Modeling the Global Annual Carbon Footprint for the Transportation Sector and a Path to Sustainability

The transportation industry’s transition to carbon neutrality is essential for addressing sustainability concerns. This study details a model for calculating the carbon footprint of the transportation sector as it progresses towards carbon neutrality. The model aims to support policymakers in estimating the potential impact of various decisions regarding transportation technology and infrastructure. It accounts for energy demand, technological advancements, and infrastructure upgrades as they relate to each transportation market: passenger vehicles, commercial vehicles, aircraft, watercraft, and trains. A technology roadmap underlies this model, outlining anticipated advancements in batteries, hydrogen storage, biofuels, renewable grid electricity, and carbon capture and sequestration. By estimating the demand and the technologies that comprise each transportation market, the model estimates carbon emissions. Results indicate that based on the technology roadmap, carbon neutrality can be achieved by 2070 for the transportation sector. Furthermore, the model found that carbon neutrality can still be achieved with slippage in the technology development schedule; however, delays in infrastructure updates will delay carbon neutrality, while resulting in a substantial increase in the cumulative carbon footprint of the transportation sector.

Restructuring hierarchical capacitated facility location problem with extended coverage radius under uncertainty

The Restructuring Facility Location Problem (RFLP) seeks to locate facilities by resizing, closing, or opening new facilities, to provide the service required by the customers, at minimum total cost. All of the previous researches on RFLP had only single-level facilities. In the present study, we addressed a new problem of restructuring hierarchical facilities, named Extended Radius bi-levels Restructuring Capacitated Facility Location Problem (ER-RCFLP), comprising main and auxiliary facilities in the first and second levels, respectively. In ER-RCFLP, there is an extended coverage radius for the main facility which customers in the coverage radius of auxiliary facilities of the main facility can get service from the main facility. A mixed-integer linear program (MILP) has been projected to minimize the restructuring cost for the introduced problem. The proposed model, not only considers both closing down and opening new facilities, and addresses the problem of resizing open facilities, but also defines the auxiliary facilities in order to minimize the total cost. The auxiliary facility increases the coverage radius of the existing and new main facilities. Also, a robust MILP model has been developed for the ER-RCFLP problem under uncertainty of demand. The impact of auxiliary facilities in the network, impact of various decisions resizing, closing or opening main facilities, and impact of demand uncertainty have been studied through six experiments and 555 sample problems. Computational results related to deterministic problems indicate that opening auxiliary facilities in a single level network enjoys 24% reduction in total cost in average. Furthermore, in an existing hierarchical network of main and auxiliary facilities, resizing of auxiliary facilities has more effect on cost reduction in comparison with closing main facilities. Moreover for the networks under uncertainty, opening new auxiliary facilities has great effect on total cost reduction of network. In addition, establishing auxiliary facilities along with resizing main facilities have more impact on network cost reduction.

Quantifying Generalized Residential Fire Risk Using Ensemble Fire Models with Survey and Physical Data

Understanding and quantifying property loss fire risk is critical to enabling decision makers in the fire field to make informed decisions. For example, the impact of various decisions made by fire departments on the fire risk in their community, while qualitatively understood by most parties, is not well quantified. Lack of quantification can lead to misdiagnosis and miscommunication on the value of services that result in sub-par resource allocations that negatively impact constituencies. Additionally, the confounding effects between fire department performance and fire risk in publicly available fire data can cause standard regression models to supply counter-intuitive conclusions based upon valid correlations (due to certain critical variables that are difficult to collect and thus unobserved). A methodology is presented that utilizes United States housing, room layout, and fire incident data, as well as experimental heat release rates, material degradation rates, and thermophysical property data in conjunction with physical fire models to estimate a community-averaged extent of fire damage in homes. Housing layout data for single family residential homes were collected from the American Housing Survey. Five home categories representing 45% of U.S. homes were selected for analysis. The U.S. Fire Administration National Fire Incident Reporting System database was used to select the distribution of initial fuels and ignition locations. Survey data was taken to specify furniture layout within the homes. A total of 5167 scenarios were developed for the combinations of home geometry, first item ignited, and furniture layout. Fire evolution was predicted for these scenarios using CFAST and coupled to a pyrolysis-inspired damage model using heat flux to targets in the homes. A damage evolution probability function was constructed for the ensemble of home and furnishing layouts and ignition distributions. This damage model was exercised in a decision analysis problem to demonstrate the utility of the methodology for community-scale resource allocation. Sensitivity studies on the model are likewise performed, indicating the largest source of uncertainty to be linked to the choice of surrogate material properties when calculating home damage. Comparison to best available data is made to assess the robustness of the model.

Modeling and Simulation of the Impact of Air Traffic Operations on the Environment

Air traffic affects the environment locally, regionally, and globally. Recent studies indicate an increased urgency to understand and mitigate the impact of air traffic on climate. Greenhouse gases, nitrogen oxides, and contrails generated by air traffic affect the climate in different and uncertain ways. Understanding the changes requires a hierarchy of models to deal with multiple disciplines, time scales ranging from a few minutes to a few hundred years, and uncertainties in modeling parameters affecting both science and policy. The models described in this paper are useful to evaluate alternate operational decisions at the national level. The modeling approach is built on the simulation and optimization techniques developed to design efficient traffic flow management strategies in the presence of uncertainties. The air traffic simulation is augmented by aircraft fuel flow, emissions, and contrail models. The integrated capability enables the evaluation of different traffic flow management concepts based on congestion, capacity, efficiency, and emissions. The impact of various decisions on climate can be evaluated by converting CO2 emissions, non-CO2 emissions, and contrails through the use of radiative forcing functions. Alternatively, the optimization results from the simulation can be used as inputs to other tools that monetize global climate impacts like the FAA’s Aviation Environmental Portfolio Management Tool for Impacts.

Role of Indian Judiciary in Upholding Gender Justice in the Matter of Right of Maintenance of Muslim Women

The aim of this paper is to analyze the role of Indian judiciary in reforming the law relating to the maintenance rights of divorced Muslim women upholding gender justice. This paper analyzes the position regarding the maintenance rights of the divorced Muslim women under traditional Muslim personal law and other legislative enactments in India. The paper proceeds with the primary and main hypothesis that Indian judiciary has played a proactive role in preventing the Muslim women from being pushed into the walkways of destitution and vagrancy on the event of their divorce and has upheld the ideal of gender justice. The impact of various decisions by Indian Judiciary in the evolution of the law of maintenance in India, from the decision of the Apex Court in Shah Bano to Shabana Bano has been discussed in this paper.

Corrections

Conventionally, software tools for the design of bioprocesses have provided only limited business-related information for decision-making. There is an industrial need to investigate manufacturing options and to gauge the impact of various decisions from economic as well as process perspectives. This paper describes the development and use of a tool to provide an assessment of whole flowsheets by capturing both process and business aspects. The tool is demonstrated by considering the issues concerned when making decisions between two potential flowsheets for a common product. A case study approach is used to compare the process and business benefits of a conventional process route employing packed chromatography beds and an alternative that uses expanded bed adsorption (EBA). The tool allows direct evaluation of the benefits of capital cost reduction and increased yield offered by EBA against penalties of using potentially more expensive EBA matrix with lower lifetimes. Furthermore, the tool provides the ability to gauge the process robustness of each flowsheet option.

A software tool to assist business-process decision-making in the biopharmaceutical industry.

Conventionally, software tools for the design of bioprocesses have provided only limited business-related information for decision-making. There is an industrial need to investigate manufacturing options and to gauge the impact of various decisions from economic as well as process perspectives. This paper describes the development and use of a tool to provide an assessment of whole flowsheets by capturing both process and business aspects. The tool is demonstrated by considering the issues concerned when making decisions between two potential flowsheets for a common product. A case study approach is used to compare the process and business benefits of a conventional process route employing packed chromatography beds and an alternative that uses expanded bed adsorption (EBA). The tool allows direct evaluation of the benefits of capital cost reduction and increased yield offered by EBA against penalties of using potentially more expensive EBA matrix with lower lifetimes. Furthermore, the tool provides the ability to gauge the process robustness of each flowsheet option.

Demand and capacity management decisions in services

Service managers are continually challenged with balancing customer demand and service capacity. Recent studies have raised awareness of various demand and capacity management practices available to services, but little numerical work has been done to identify how these decisions work together and how they relate to one another. For instance, reducing prices may attract customers during a slow period, but the extent of impact this should have on cross‐training staff is not clear. A simulation based on theoretical and empirical insights explores the impact of various decisions on profitability and operations. The decisions modelled include the impact of: automation, customer participation, cross training employees, informing customers about the operation, and others. It is shown that demand and capacity decisions do indeed impact on each other – sometimes in ways that are not initially obvious. Results provide useful thought‐starters for service managers striving to improve their operations.

A SIMULATION STUDY TO EVALUATE PROC MIXED ANALYSIS OF REPEATED MEASURES DATA

Experiments with repeated measurements are common in pharmaceutical trials, agricultural research, and other biological disciplines. Many aspects of the analysis of such experiments remain controversial. With increasingly sophisticated software becoming available, e.g. PROC MIXED, data analysts have more options from which to choose, and hence more questions about the value and impact of these options. These dilemmas include the following. MIXED offers a number of different correlated error models and several criteria for choosing among competing models. How do the model selection criteria behave? How is inference affected if the correlated error model is misspecified? Some texts use random between subject error effects in the model in addition to correlated errors. Others use only the correlated error structure. How does this affect inference? MIXED has several ways to determine degrees of freedom, including a new option to use Kenward and Roger's procedure. The Kenward-Roger procedure also corrects test statistics and standard errors for bias. How do the various degree-of-freedom options compare? When is the bias serious enough to worry about and how well does the Kenward-Roger option work? Some models are prone to convergence problems. When are these most likely to occur and how should they be addressed? We present the results of several simulation studies conducted to help understand the impact of various decisions on the small sample behavior of typical situations that arise in animal health and agricultural settings.