An Analysis on the Markovian Model for Production Management and Establishment of New Pharmacies Using Hyper Graph

Abstract

Stochastic models are mainly used to rectify the real-life problems in different areas of the busy world such as marketing, trading, networking, communicating media, and challenging biomedical inventions, for the survival of mankind. Any business which runs on the basis of getting more profit with less investment includes all the resources for production and the man power too, during the current century. To bring the equality in business gain and man power, many stochastic models have been introduced since the 1940s. The different states and the steady state probabilities are discussed by the Markov models to overcome the insufficiency of man power. An Iterative Hidden Markov Model has been introduced for effective production by considering the observable states depend on hidden states under the circumstances of real-world probabilities are known and unknown. This paper mainly deals with the various machines, pharmacies, and different methods, namely, Nonadjacency Search Method and Adjacency Search Method applied through the Hyper graph. Comparison made between Nonadjacency Search Method and Adjacency Search Method through hyper assignments using cliques helps us to obtain tremendous profit through effective production and to start new pharmacies, instead of predictions through computational methods. Results were discussed based on the information obtained from Apollo pharmacies.