Analyzing the scheduling system stage of production system life cycle

The main objective of this paper is to identify and understand the relationship dynamics among the quality enabled factors affecting the scheduling stage of production system life cycle. For this purpose, Interpretative structural modelling (ISM) approach has been utilized for developing the relationships among the various factors of the scheduling system stage. Afterwards, MICMAC (Matriced Impacts Croises Multiplication Appliqueeaun Classement) analysis has been carried out in order to classify the factors into different categories and to disclose the direct and indirect effects of each factor on the scheduling system. It is an approach for refining the decision making in the scheduling stage of production system life cycle

Application of preference selection index method for decision making over the design stage of production system life cycle

The life cycle of production system shows the progress of production system from the inception to the termination of the system. During each stage, mainly in the design stage, certain strategic decisions have to be taken. These decisions are more complex as the decision makers have to assess a wide range of alternatives based on a set of conflicting criteria. As the decision making process is found to be unstructured, characterized by domain dependent knowledge, there is a need to apply an efficient multi-criteria decision making (MCDM) tool to help the decision makers in making correct decisions. This paper explores the application of a novel MCDM method i.e. Preference selection index (PSI) method to solve various decision-making problems that are generally encountered in the design stage of production system life cycle. To prove the potentiality, applicability and accuracy of PSI method in solving decision making problem during the design stage of production system life cycle, five examples are cited from the literature and are compared with the results obtained by the past researchers

Quantifying barriers to implementing Total Quality Management (TQM)

Total Quality Management (TQM) is a philosophy that delivers long-term benefits in terms of profitability, customer satisfaction and quality of products. Different organisations work for TQM implementation and utilise their resources to achieve the anticipated benefits. However, it is generally experienced that TQM implementation is a hard and very painful process. There are certain barriers that inhibit the successful implementation of TQM. In the present work, an attempt was made to develop a mathematical model of these barriers using a Graph Theoretic Approach (GTA). An index of barriers in TQM is proposed which evaluates the inhibiting power of these barriers. [Received 02 October 2008; Revised 17 March 2009; Revised 25 May 2009; Accepted 13 June 2009]total quality management; TQM implementation; implementation barriers; intensity: graph theory; mathematical modelling.

GTA-based framework for evaluating the role of design parameters in cogeneration cycle power plant efficiency

This paper presents a methodology based on graph theoretic approach (GTA) to design a new cogeneration cycle power plant (CGCPP), improvement of existing plant and comparison of two real life operating cogeneration cycle power plants. Different combinations may be suggested by a manufacturer to an organization for selecting or improving the efficiency of a power plant. This paper identifies various design parameters affecting cogeneration cycle power plant efficiency. All these parameters are interacting with each other by different amounts. An attempt has been made to develop a mathematical model of CGCPP from these interacting parameters using GTA. A CGCPP efficiency index is proposed which evaluates the influence power of these parameters

Development of reliability index for combined cycle power plant using graph theoretic approach

A systematic approach based on graph theory and matrix method is developed ingeniously for the evaluation of reliability index for a Combined Cycle Power Plant (CCPP). In present work CCPP system is divided into six subsystems. Consideration of all these subsystems and their interrelations are rudiment in evaluating the index. Reliability of CCPP is modeled in terms of a Reliability Attributes Digraph. Nodes in digraph represent system reliability and reliability of interrelations is represented by edges. The digraph is converted into one-to-one matrix called as Variable System Reliability Permanent Matrix (VPM-r). A procedure is defined to develop variable permanent function for reliability (VPF-r) from VPM-r. Reliability index of CCPP system is obtained from the permanent of the matrix by substituting numerical values of the attributes and their interrelations. A higher value of index implies better reliability of the system. The proposed methodology is illustrated step-by-step with the help of two examples

GTA modeling of combined cycle power plant efficiency analysis

AbstractA methodology based upon graph theory and matrix method is developed for the efficiency analysis of a Combined Cycle Power Plant (CCPP) with a view that a person at managerial level may decide regarding efficiency improvement without in-depth knowledge of conventional methods of thermal efficiency evaluation. For the analysis, CCPP is divided into six sub-systems and interdependency is identified to develop system structure digraph. Sub-system efficiencies are further dependent on numerous parameters which are interdependent. Therefore, methodology developed at system level is extended to sub-system level. Performance parameter digraph is developed at sub-system level by intriguing inheritance and interdependencies of parameters. Digraph is converted into matrix form and permanent function is developed. The value of permanent function in real time situation is compared with design condition to obtain Relative Efficiency Index (REI) at sub-system level. REI at sub-system level is guidance for deviation of efficiency index from its design value

History and Spread of Viruses (COVID-19) and Associated Demographic and Clinical Parameters – A systematic review and meta-analysis

COVID-19 made a huge impact on the world due to its rapid transmission and no treatments being available for it. The virus affected more people and spread to various countries than what was predicted when COVID-19 initially began spreading. There have been numerous pandemics and epidemics in the 21st century yet COVID-19 has affected more people and spread widely. The primary objective of the study was to explore history, spread and associated parameters of existing viruses especially COVID-19. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was followed for a systematic search to identify eligible published articles. Clinical data, regarding COVID-19 patients, was obtained from previously published articles. The main cause of COVID-19 spreading rapidly was noted to be due to a high percentage of asymptomatic patients, transmission being air-borne, and the lack of knowledge and preventative measures being implemented when the virus began spreading. The common co-morbidity that found in patients was Diabetes Mellitus, Hypertension, and Coronary Heart Disease. The common symptoms, found through the Meta-analysis, that the patients faced included cough (55.4%), fever (68.4%), fatigue (20.3%), and shortness of breath (18.1%). The proportion of asymptotic positive cases was measured 58.3% (95%CI: 24.7% – 87.9%) while mortality proportion was found to be 6.7% (fixed-effect model) and 13.4% (random-effect model). The Meta-analysis indicated that a higher percentage of males were affected by COVID-19 than females and more patients are found to be asymptomatic. Moreover, the mortality rate of patients that have had COVID-19 was found to be low.&nbsp