Career Advancement of MBAs: A study on the effect of personal, professional,organisational and environmental variables

School of Management Studies, Cochin University of Science and Technology

A study on the organisational commitment of professionals with respect to various aspects of organisational socialisation

Organisational commitment of various groups of professionals seems to be moderated by the differences of the groups as well as of the socialisation experiences Demographic variable ‘age’ and the semi-structural variable ‘experience’ cause a difference in the level of commitment. Similarly, the professional and organisational differences moderate the level of commitment. From the point of view of the organisations, the socialisation experiences if differ can be used as tool to process and enhance the level of commitment of professionals of various groups.The ‘Socialisation effect’ does not depend on the professional or demographic differences. Socialisation level is moderated only by the organisational specialities. It is purely an organisational variable. However, the difference in the socialisation levels as found among the professionals can cause a significant difference in the levels of organisational commitment of professionals

Commitment of women industrial workers with special reference to electronics industry in Kerala

In this study of workers of the two industrial units, it has been found that the women workers are more committed to the organization than the men workers. The organizational commitment has been a popular research topic among organizational sociologists, industrial psychologists and labour relations specialists. Much of the early research was directed on organizational commitment of men workers and studies on organizational commitment of women workers were less when compared to that of men workers. The results of this study is partially supported by the findings of Aranya and Jacobson (1975) who found that parental responsibility is linked with maintaining stable patterns of employment.

Applying Data mining using Statistical Techniques for Career Selection

For years, choosing the right career by monitoring the trends and scope for different career paths have been a requirement for all youngsters all over the world. In this paper we provide a scientific, data mining based method for job absorption rate prediction and predicting the waiting time needed for 100% placement, for different engineering courses in India. This will help the students in India in a great deal in deciding the right discipline for them for a bright future. Information about passed out students are obtained from the NTMIS ( National technical manpower information system ) NODAL center in Kochi, India residing in Cochin University of science and technology

Reinforcement Learning Solution for Unit Commitment Problem Considering Minimum Start Up and Shut Down Times

Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems

Reinforcement Learning solution for Unit Commitment Problem through pursuit method

Unit commitment is an optimization task in electric power generation control sector. It involves scheduling the ON/OFF status of the generating units to meet the load demand with minimum generation cost satisfying the different constraints existing in the system. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision task and Reinforcement Learning solution is formulated through one efficient exploration strategy: Pursuit method. The correctness and efficiency of the developed solutions are verified for standard test systems

The Social Commitment Of The New Leftist Poets

Hindi