Browsing by Author "Paul, Kizito Mubiru"

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    Modeling Campaign Optimization Strategies in Political Elections under Uncertainty
    (International Journal of Scientific Research in Mechanical and Materials Engineering, 2018) Christopher, Senfuka; Paul, Kizito Mubiru; Maureen, N. Ssempijja
    In most political campaigns,the overall goal of every candidate is to maximize the number of voters during the election exercise.In such an effort,cost effective methods in choosing the optimal campaign strategy areparamount.In this paper, a mathematical model is proposed that optimize campaign strategies of a political candidate.Considering uncertainty in voter support and cost implications in holding political rallies,we formulate a finite state markov decision process model where states of a markov chain represent possible states of support among voters.Using daily equal intervals,thecandidates‟s decision of whether or not to campaign and hold a political rally at a given location were made using discrete time Markov chains and dynamic programming over a finite period planning horizon.Empirical data was collected from two locations on a daily basis during the campaign exercise.The data collected was analyzed and tested to establish the optimal campaign strategy and costs at the respective locations.Results from the study indicated the existence of an optimal state-dependent campaign strategy and costs at the respective political rally locations.
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    Stochastic Modeling of Internet Service for Profit Optimization in Uganda
    (IST-Africa, 2018) Christopher, Senfuka; Paul, Kizito Mubiru; Maureen, N. Ssempijja
    We consider an internet cafe faced with an optimal choice of bandwidth for internet users under stochastic stationary demand. The choice is made over uniformly time horizons with a goal of optimizing profits. Considering customer demand, price and operating costs of internet service, we formulate a finite state Markov decision process model where states of a Markov chain represent possible states of demand for internet service. A profit matrix is generated; representing the long run measure of performance for the Markov decision process problem. The problem is to determine an optimal bandwidth adjustment policy so that the long run profits are maximized for a given state of demand. The bandwidth adjustment policies are determined using dynamic programming over a finite period planning horizon. Results from a case study demonstrate the existence of an optimal statedependent option for bandwidth adjustment and profits in providing internet service.