Conjoint Analysis for Selecting the Ingredients Levels of Fortified Beverage

This research paper concentrated on the “Conjoint Analysis for Selecting the Ingredients Levels of Fortified Beverage”. Conjoint analysis is a multivariate technique used specifically to understand how consumers develop preferences for products or services and to formulate predictions about ingredients levels towards product concepts and it is also called as trade-off analysis. The fortified beverage was prepared with various levels of ingredients such as Carrot (5%, 10% and 15%), Moringa (5%,10% and 15%), Mushroom (3%,6% and 9%), Dates (1%,2% and 3%) and Seaweed (1%,2% and 3%). Since, this large number of combinations led to non-responsible of consumers and improper results may be obtained. Hence, the conjoint analysis for selecting the best ingredient levels of ideal fortified beverage is based on the utility estimation and relative importance of attributes and was found that it should have the following attributes combination: Carrot – 15 per cent, Mushroom – 6 Per cent, Moringa – 5 per cent, Dates – 2 per cent and Sea Weed – 1 per cent.

Fighting Wildfires under Uncertainty - A Sequential Resource Allocation Approach

Standard disaster response involves using drones (or helicopters) for reconnaissance and using people on the ground to mitigate the damage. In this paper, we look at the problem of wildfires and propose an efficient resource allocation strategy to cope with both dynamically changing environment and uncertainty. In particular, we propose Firefly, a new resource allocation algorithm, that can provably achieve optimal or near optimal solutions with high probability by first efficiently allocating observation drones to collect information to reduce uncertainty, and then allocate the firefighting units to extinguish fire. For the former, Firefly uses a combination of maximum set coverage formulation and a novel utility estimation technique, and it uses a knapsack formulation to calculate the allocation for the latter. We also demonstrate empirically by using a real-world dataset that Firefly achieves up to 80-90% performance of the offline optimal solution, even with a small amount of drones, in most of the cases.