Analysis of Some Single and Two Commodity Inventory Problems

This thesis is devoted to the study of some stochastic models in inventories. An inventory system is a facility at which items of materials are stocked. In order to promote smooth and efficient running of business, and to provide adequate service to the customers, an inventory materials is essential for any enterprise. When uncertainty is present, inventories are used as a protection against risk of stock out. It is advantageous to procure the item before it is needed at a lower marginal cost. Again, by bulk purchasing, the advantage of price discounts can be availed. All these contribute to the formation of inventory. Maintaining inventories is a major expenditure for any organization. For each inventory, the fundamental question is how much new stock should be ordered and when should the orders are replaced. In the present study, considered several models for single and two commodity stochastic inventory problems. The thesis discusses two models. In the first model, examined the case in which the time elapsed between two consecutive demand points are independent and identically distributed with common distribution function F(.) with mean  (assumed finite) and in which demand magnitude depends only on the time elapsed since the previous demand epoch. The time between disasters has an exponential distribution with parameter . In Model II, the inter arrival time of disasters have general distribution (F.) with mean  ( ) and the quantity destructed depends on the time elapsed between disasters. Demands form compound poison processes with inter arrival times of demands having mean 1/. It deals with linearly correlated bulk demand two Commodity inventory problem, where each arrival demands a random number of items of each commodity C1 and C2, the maximum quantity demanded being a (< S1) and b(

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Child labour in India

This thesis entitled “Child labour in india”Children are "supremely important assets" of the nation, India proudly asserts in the National Policy for Children (1974) gracefully acknowledging that they are future citizens on whose shoulders the destiny of our nation rests.Childhood is a time of discovery as the world and all it contains are new to children. It is a time of excitement and anticipation. lt is a time of dreams and fantasies. And it is a time of receiving love and appreciation. Paradoxically, a picture of contrast is a common experience in India as a vast majority of children who are starved of basic needs of nutrition, health and education are made to work at an early age in exploitative conditions. The specter of child labour is a glaring anomaly in a country graciously adorning human right. In an exposition of the problem involving human rights abuse, Chapters from Two to Five of this study have shot into focus the human rights jurisprudence with special reference to the rights of children. Children have a particular identity as children and they also have a universal identity as human beings.The concern for mankind expressed unequivocally and transcending the globe will be real and moving and not mere rhetoric and ritual if and only when it begins with children, as, to quote the words of Nehru, the human being counts much more as a child than as a grown up.The first three of these rights namely right to health, right to nutritive food and right to education are dealt with in Chapter Four. Finally, the positive effects of education have been sketched in that chapter to impress upon its significance in the development of human capitals.legitimization. The theme of legitimacy was rationalised on the ground of poverty as a strategy for achieving eradication of child labour ultimately by enforcing minimum wages, shorter working hours, leave compensation, non-formal education etc., as the employer would soon discover that child labour is not cheap and would be obliged to substitute adult labour. However, humanising the work life is only a promise to the detriment of children as the Act of 1986 enacted as a part of the new strategy is nearingcompletion of a decade of existence but nowhere near the fulfilment of the mission.As similar urge is more necessary and overdue, it has been suggested that a special body be established with all powers for cognisance of human rights abuse of children.It is proposed to conclude this study with a brief summary of the inferences drawn from the foregoing chapters along with a few suggestions emerging out of those inferences

Influence of a Salt Water Regulator on the Survival Response of Salmonella Paratyphi in Vembanadu Lake: India

Contamination of environmental water by pathogenic microorganisms and subsequent infections originated from such sources during different contact and non- contact recreational activities are a major public health problem worldwide particularly in developing countries. The main pathogen frequently associated with enteric infection in developing countries are Salmonella enterica serovar typhi and paratyphi. Although the natural habitat of Salmonella is the gastrointestinal tract of animals, it find its way into natural water through faecal contamination and are frequently identified from various aquatic environments (Baudart et al., 2000; Dionisio et al., 2000; Martinez -Urtaza et al., 2004., Abhirosh et al., 2008). Typhoid fever caused by S. enterica serotype typhi and paratyphi are a common infectious disease occurring in all the parts of the world with its highest endemicity in certain parts of Asia, Africa, Latin America and in the Indian subcontinent with an estimated incidence of 33 million cases each year with significant morbidity and mortality (Threlfall, 2002). In most cases the disease is transmitted by polluted water (Girard et al., 2006) because of the poor hygienic conditions, inadequate clean water supplies and sewage treatment facilities. However in developed countries the disease is mainly associated with food (Bell et al., 2002) especially shellfish (Heinitz et al., 2000

Attribute reduction and missing value imputing with ANN: prediction of learning disabilities

Learning disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 10% of children enrolled in schools. There is no cure for learning disabilities and they are lifelong. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Just as there are many different types of LDs, there are a variety of tests that may be done to pinpoint the problem The information gained from an evaluation is crucial for finding out how the parents and the school authorities can provide the best possible learning environment for child. This paper proposes a new approach in artificial neural network (ANN) for identifying LD in children at early stages so as to solve the problems faced by them and to get the benefits to the students, their parents and school authorities. In this study, we propose a closest fit algorithm data preprocessing with ANN classification to handle missing attribute values. This algorithm imputes the missing values in the preprocessing stage. Ignoring of missing attribute values is a common trend in all classifying algorithms. But, in this paper, we use an algorithm in a systematic approach for classification, which gives a satisfactory result in the prediction of LD. It acts as a tool for predicting the LD accurately, and good information of the child is made available to the concerned

Social Structural Determinants of Education among Tribes in Kerala

The study is focused on education of tribes particularly the problem of high dropout rate existing among the tribal students at school level. Scheduled Tribe is one of the marginalized communities experiencing high level of educational deprivation. The analysis of the study shows that the extent of deprivation existing among STs of Kerala is much higher compared to that of other communities. The present study covered tribes of three tribal predominant districts of Kerala such as Idukki, Palakkad and Wayanad. Out of the 35 tribal communities in the State, 17 of them are concentrated in these districts. Tribes concentrated in Idukki include Muthuvans, Malai Arayan, Uraly, Mannan and Hill Pulaya. The present study analyzed dropouts situation in tribal areas of Kerala by conducting Field Survey among dropout and non-dropout students at school level. High dropouts among STs persist due to many problems which are of structural in nature. Important problems faced by the tribal students that have been analyzed, this can be classified as economic, social, cultural and institutional. It is found that there exists high correlation between Income and expenditure of the family with the well-being of individuals. Significant economic factors are poverty and financial indebtedness of the family. Some of the common cultural factors of tribes are Nature of Habitation, Difference in Dialect and Medium of Instruction etc. Social factors analyzed in the study are illiteracy of parents, migration of family, family environment, motivation by parents, activities engaged in for helping the family and students’ lack of interest in studies. The analysis showed that all these factors except migration of the family, are affecting the education of tribal students. Apart from social, economic and cultural factors, there are a few institutional factors which will also influence the education of tribal students. Institutional factors analyzed in the study include students’ absenteeism, irregularity of teachers, attitude of non-tribal teachers and non-tribal students, infrastructure facilities and accessibility to school. The study found irregularity of students and accessibility to school as significant factors which determine the dropout of the students.