Planning And Operation Problems Of Indian Major Ports With Special Emphasis On Cochin Port

Rapid changes in the technological environment of marine logistics and the increasing integration of waterborne, air and land transport systems have fostered a revolution in the design and operations of transport vehicles, cargo handling technology, and terminal facilities. This in turn has caused major changes in the functions of and uses of ports. From literature, it was found that these changes were very slow in case of Indian ports and the performances of port operations were poor when compared with similar ports in the same region. It was also found that a very few studies were conducted to identify the reasons for slow improvements in the performances of Indian major ports. In this thesis, an attempt is made to find out the operational problems of Indian major ports and to analyze the reasons for it. Some solutions have also been found out using management tools

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