Experimental Study on an Efficient Dengue Disease Management System

Abstract

Dengue has become a serious health hazard in Sri Lanka with the increasing cases and loss of human lives. It is necessary to develop an efficient dengue disease management system which could predict the dengue outbreaks, plan the countermeasures accordingly and allocate resources for the countermeasures. We have proposed a platform for Dengue disease management with following modules: (1) a prediction module to predict the dengue outbreak and (2) an optimization algorithm module to optimize hospital staff according to the predictions made on future dengue patient counts. This paper focuses on the optimization algorithm module. It has been developed based on two approaches: (1) Genetic Algorithm (GA) and (2) Iterated Local Search (ILS). We are presenting the performances of our optimization algorithm module with a comparison of the two approaches. Our results show that the GA approach is much more efficient and faster than the ILS approach.

Sustainable Manufacturing: Application of Optimization to Textile Manufacturing Plants

“…We have used an approximation (heuristic) based approach: Genetic Programming (GP), to optimize the identified work flows. The GP approach is a well-known method in the planning and scheduling context [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16].Moreover, they have been widely used for research works related to manufacturing planning systems.…”