Browsing by Author "Rankothge, W.H."

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Real-Time Decision Optimization Platform for Airline Operations
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Weerasinghe, P.S.R.; Ranasinghe, R.A.M.D.K.; Mahanthe, M.M.V.R.B.; Samarakoon, P.G.C.B.; Rankothge, W.H.; Kasthurirathna, D.
With close to 4 billion origin-destination passenger journeys worldwide, airline operations have become a crucial factor in the global economy. With the increasing number of journeys and passengers, managing the daily operations of airlines have become a complicated task. We have proposed a real-time decision optimization platform for airline operations with the following subsystems: (1) determine the optimum path for a flight, (2) optimum fleet assignment, (3) optimum gate allocation, (4) optimum crew allocation. We have used an approximation (heuristics) based optimization approach: Genetic Programming (GP) to implement the modules. The results of our proposed platform illustrate that, the decision-making process of Airline Operations Control Center (AOCC) can be optimized, and dynamic change requirements can be accommodated.
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Supply and Demand Planning for Water: A Sustainable Water Management System
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Athapaththu, A.M.H.N.; Illeperumarachchi, D.U.S.; Herath, H.M.K.U.; Jayasinghe, H.K.; Rankothge, W.H.; Gamage, N.
Sustainable water management requires maintaining the balance between the demand and supply, specifically addressing water demand in urban, agricultural, and natural systems. Having an insight on water supply forecasting and water consumption forecasting, will be useful to generate an optimal water distribution plan. A platform that targets the sustainable water management concepts for domestic usage and paddy cultivation is proposed in this paper, with the following components: (1) forecasting water levels of reservoirs, (2) forecasting water consumption patterns, and (3) optimizing the water distribution. We have used Recurrent Neural Network (RNN) and, Long Short-Term Memory (LSTM) for forecasting modules and, Genetic Programming (GP) for optimizing water distribution. Our results show that, using our proposed modules, sustainable water management related services can be automated efficiently and effectively.